[2024] 1200+ Free Computer Science Courses from World’s Top Universities

Combining leading university rankings to find the best computer science courses available online.

Manoel Cortes Mendez & Suparn Patra

Sep 24th, 2024

In this article, we’ve compiled 1200+ online courses offered by the 60 best universities in the world for studying computer science in 2024.

Manoel first built the list in 2020 using a data-driven approach that we have used each year, including 2024. You can find the methodology below. But if you’d rather go straight to the course list, click here.

Methodology

First, Manoel identified the leading world university rankings. Since we were interested in computer science specifically, he looked at their latest computer science rankings.

For the 2024 update, Suparn used the same sources and methodology to find the top 60 universities offering online computer science courses.

Here are the rankings used:

QS: World University Ranking 2024 — Computer Science & IT
Times Higher Education: World University Ranking 2024 — Computer Science
Shanghai Ranking: Academic Ranking of World Universities 2023 — Computer Science & Engineering

Then, we crawled and scraped each ranking.

Now that we had some data, we used Jupyter with Python to process it. We combined the three rankings into one by averaging the position of each university in each ranking. Then, we filtered out the universities that didn’t offer online courses, and limited the list to the top 60 institutions — the cream of the crop.

Top 10 CS universities: MIT, Stanford, CMU, Oxford, UC Berkley, Harvard, Tsinghua, NUS, ETH Zurich, NTU. — Combined ranking: top 10 universities for studying computer science in 2024

As you can see above, we found that the top three institutions are #1 MIT, #2 Stanford, and #3 Carnegie Mellon.

Finally, we used the Class Central database, with its 250K online courses, to find all the computer science courses offered by the universities in the ranking.

The end result is a list of 1200+ online courses offered by the 60 best universities in the world for studying computer science in 2024.

Stats

Enrollments range from 10 to over 13 million, with 20 courses exceeding 1 million enrollments
Altogether, they have over 110M enrollments, with an average of 155K enrollments
1180 courses are in English, 39 Chinese, 15 Spanish, 13 Arabic, 12 French, 12 Korean, 7 Russian, 3 Portuguese, 3 German, 1 Dutch, and 1 Japanese
Together, they account for more than 78K reviews at Class Central, with an average of 216 reviews
Average rating: 4.07 out of 5.0
All these courses are free or can be audited for free
240 courses are beginner level, 281 are intermediate level, and 70 are advanced level.

More Courses

The full list is split into subjects. Click on a subject below to go to the relevant section. With over 1200 courses to pick from, I hope you find something you like. But if these aren’t enough, check out Class Central’s catalog of 250K online courses or our thematic collections:

Programming (267)

Programming for Everybody (Getting Started with Python) from University of Michigan ★★★★★(44479)
Python Data Structures from University of Michigan ★★★★★(16763)
Using Python to Access Web Data from University of Michigan ★★★★★(5744)
Using Databases with Python from University of Michigan ★★★★★(4709)
Python and Statistics for Financial Analysis from The Hong Kong University of Science and Technology ★★★★☆(573)
Computing in Python I: Fundamentals and Procedural Programming from Georgia Institute of Technology ★★★★★(254)
CS106B, Programming Abstraction in C++ from Stanford University ★★★★★(164)
Learn to Program: The Fundamentals from University of Toronto ★★★★★(110)
Computing in Python IV: Objects & Algorithms from Georgia Institute of Technology ★★★★★(107)
Data Science and Agile Systems for Product Management from University System of Maryland ★★★★★(97)
Computing in Python II: Control Structures from Georgia Institute of Technology ★★★★★(59)
Introduction to HTML5 from University of Michigan ★★★★☆(52)
Computing in Python III: Data Structures from Georgia Institute of Technology ★★★★★(48)
Programming Mobile Applications for Android Handheld Systems: Part 1 from University of Maryland, College Park ★★★★☆(41)
Functional Program Design in Scala from École Polytechnique Fédérale de Lausanne ★★★★★(40)
Introduction to Programming for the Visual Arts with p5.js from University of California, Los Angeles ★★★★★(36)
Programming Languages, Part A from University of Washington ★★★★★(27)
CS50’s Web Programming with Python and JavaScript from Harvard University ★★★★★(26)
Database Systems – Cornell University Course (SQL, NoSQL, Large-Scale Data Analysis) from Cornell University ★★★★★(24)
HTML, CSS, and Javascript for Web Developers from Johns Hopkins University ★★★★★(20)
Object Oriented Programming in Java from University of California, San Diego ★★★★☆(16)
Programming Mobile Applications for Android Handheld Systems: Part 2 from University of Maryland, College Park ★★★★☆(15)
Introduction to CSS3 from University of Michigan ★★★★★(14)
Python Data Structures from University of Michigan ★★★★★(12)
Interactivity with JavaScript from University of Michigan ★★★★☆(12)
Using Python for Research from Harvard University ★★★★☆(12)
Code Yourself! An Introduction to Programming from University of Edinburgh ★★★★☆(11)
CS50’s Introduction to Programming with Python from Harvard University ★★★★★(11)
Learn to Program: Crafting Quality Code from University of Toronto ★★★★☆(10)
Programming for Everybody (Getting Started with Python) from University of Michigan ★★★★★(9)
Advanced Styling with Responsive Design from University of Michigan ★★★★☆(8)
MATLAB and Octave for Beginners from École Polytechnique Fédérale de Lausanne ★★★★☆(8)
Introduction To Swift Programming from University of Toronto ★☆☆☆☆(7)
Introduction to Java Programming – Part 1 from The Hong Kong University of Science and Technology ★★★☆☆(6)
CS50’s Introduction to Programming with Scratch from Harvard University ★★★★☆(6)
Parallel programming from École Polytechnique Fédérale de Lausanne ★★★★☆(6)
Creating Video Games from Massachusetts Institute of Technology ★★★★☆(6)
Capstone: Retrieving, Processing, and Visualizing Data with Python from University of Michigan ★★★☆☆(6)
Single Page Web Applications with AngularJS from Johns Hopkins University ★★★★★(5)
The Computing Technology Inside Your Smartphone from Cornell University ★★★★★(5)
Web Coding Fundamentals: HTML, CSS and Javascript from National University of Singapore ★★★★★(5)
Software Development Process from Georgia Institute of Technology ★★★★☆(5)
Introduction To MATLAB Programming (Fall 2011) from Massachusetts Institute of Technology ★★★★★(5)
CS 193a: Android App Development Winter 2019 from Stanford University ★★★★★(5)
Introduction à la programmation orientée objet (en C++) from École Polytechnique Fédérale de Lausanne ★★★★☆(4)
Building Web Applications in PHP from University of Michigan ★★★★★(3)
Introduction to Structured Query Language (SQL) from University of Michigan ★★★★☆(3)
Understanding and Visualizing Data with Python from University of Michigan ★★★★★(3)
Databases: Relational Databases and SQL from Stanford University ★★★★☆(3)
Mastering the Software Engineering Interview from University of California, San Diego ★★★★☆(3)
Initiation à la programmation (en C++) from École Polytechnique Fédérale de Lausanne ★★★★★(3)
iOS App Development Basics from University of Toronto ★★★★☆(2)
CS50’s Mobile App Development with React Native from Harvard University ★★★★★(2)
Software Engineering Essentials from Technische Universität München (Technical University of Munich) ★★★★☆(2)
App Design and Development for iOS from University of Toronto ★★★☆☆(2)
Python Basics from University of Michigan ★★★★★(2)
CS50’s Introduction to Databases with SQL from Harvard University ★★★★☆(2)
التفاعل مع لغة البرمجة جافا سكريبت from University of Michigan ★★★☆☆(2)
R البرمجة باستخدام لغة from Johns Hopkins University ★★★★☆(2)
Programmation pour tous (mise en route de Python) from University of Michigan ★★★★☆(2)
Make Your Own App from Technische Universität München (Technical University of Munich) ★★★★★(1)
Building Database Applications in PHP from University of Michigan ★★★★★(1)
Database Design and Basic SQL in PostgreSQL from University of Michigan ★★★★★(1)
JSON and Natural Language Processing in PostgreSQL from University of Michigan ★★★★★(1)
Database Systems Concepts & Design from Georgia Institute of Technology ★★★★☆(1)
Building Web Applications in Django from University of Michigan ★★★★★(1)
Automated Software Testing: Model and State-based Testing from Delft University of Technology ★★★★★(1)
Fitting Statistical Models to Data with Python from University of Michigan ★★★★★(1)
Web Design for Everybody Capstone from University of Michigan ★★★★☆(1)
Inferential Statistical Analysis with Python from University of Michigan ★★★★★(1)
Lernen objekt-orientierter Programmierung from Technische Universität München (Technical University of Munich) ★★★★★(1)
Python Functions, Files, and Dictionaries from University of Michigan ★★★★★(1)
Programming with Scratch from The Hong Kong University of Science and Technology ★★☆☆☆(1)
¡A Programar! Una introducción a la programación from University of Edinburgh ★★★★★(1)
البرمجة للجميع – بدء استخدام بايثون from University of Michigan ★★★★★(1)
Initiation à la programmation (en Java) from École Polytechnique Fédérale de Lausanne ★★★★★(1)
Introduction à la programmation orientée objet (en Java) from École Polytechnique Fédérale de Lausanne ★★★★★(1)
Programación para todos (empezando con Python) from University of Michigan ★★★★☆(1)
计算导论与C语言基础 from Peking University ★★★★☆(1)
Stanford Seminar – Optional Static Typing for Python from Stanford University ★★★★★(1)
Программирование для всех (начало работы с Python) from University of Michigan ★★★★★(1)
15-721 Advanced Database Systems (Spring 2017) from Carnegie Mellon University ★★★★☆(1)
15-721 Advanced Database Systems (Spring 2019) from Carnegie Mellon University ★★★★★(1)
Seven Databases in Seven Weeks (Fall 2014) from Carnegie Mellon University ★★★★★(1)
Stanford Webinar – Cloud Computing: What’s on the Horizon with Dr. Timothy Chou from Stanford University ★★★☆☆(1)
JavaScript, jQuery, and JSON from University of Michigan
Automated Software Testing: Unit Testing, Coverage Criteria and Design for Testability from Delft University of Technology
Quantitative Methods for Biology from Harvard University
Build Your Own iOS App from University of Toronto
Software Engineering: Implementation and Testing from The Hong Kong University of Science and Technology
Introduction to Python Programming from University of Pennsylvania
Databases: Advanced Topics in SQL from Stanford University
Introduction to Java Programming – Part 2 from The Hong Kong University of Science and Technology
Software Analysis & Testing from Georgia Institute of Technology
Introduction to Java and Object-Oriented Programming from University of Pennsylvania
Using JavaScript, JQuery, and JSON in Django from University of Michigan
Software Engineering: Software Design and Project Management from The Hong Kong University of Science and Technology
Database Design and Basic SQL in PostgreSQL from University of Michigan
Creating Virtual Reality (VR) Apps from University of California, San Diego
R Programming Fundamentals from Stanford University
Programming in C from University of Michigan
Programming Languages Ⅰ from Korea Advanced Institute of Science and Technology
Data Structures in C from University of Michigan
Big Ideas in Programming: Expressing Yourself with Python from University of Michigan
Software Engineering: Modeling Software Systems using UML from The Hong Kong University of Science and Technology
Web App Development with the Power of Node.js from Technische Universität München (Technical University of Munich)
Developing Android Apps with App Inventor from The Hong Kong University of Science and Technology
Web Application Technologies and Django from University of Michigan
Using JavaScript and JSON in Django from University of Michigan
Data Collection and Processing with Python from University of Michigan
UML Class Diagrams for Software Engineering from KU Leuven University
Building Objects in C from University of Michigan
Mobile Application Experiences from Massachusetts Institute of Technology
How Virtual Reality Works from University of California, San Diego
Programming Languages Ⅱ from Korea Advanced Institute of Science and Technology
The Power of Object-Oriented Programming from University of Michigan
Intermediate PostgreSQL from University of Michigan
CS50’s Introduction to Programming with R from Harvard University
Answering Interesting Questions with Data from University of Michigan
Database Architecture, Scale, and NoSQL with Elasticsearch from University of Michigan
Inheritance and Data Structures in Java from University of Pennsylvania
Databases: Modeling and Theory from Stanford University
3D Graphics in Android: Sensors and VR from Imperial College London
Exploring C from University of Michigan
Introduction to Neurohacking In R from Johns Hopkins University
Using JavaScript and JSON in Django from University of Michigan
Introduction to Android graphics from Imperial College London
Introduction to Object-Oriented Programming with Java III: Exceptions, Data Structures, Recursion, and GUIs from Georgia Institute of Technology
Worldbuilding for Video Games from The University of British Columbia
JSON and Natural Language Processing in PostgreSQL from University of Michigan
Statistical Learning with Python from Stanford University
Real-Time Audio Signal Processing in Faust from Stanford University
Intermediate PostgreSQL from University of Michigan
Introduction to Javascript and Ajax: Building Web Apps from Johns Hopkins University
Cloud Applications from Georgia Institute of Technology
Databases: Semistructured Data from Stanford University
Introduction to Object-Oriented Programming with Java I: Foundations and Syntax Basics from Georgia Institute of Technology
Global Software Development from Delft University of Technology
Advanced App Development in Android Capstone from Imperial College London
Practical Python for AI Coding 1 from Korea Advanced Institute of Science and Technology
Debugging: Hunting and Squashing Bugs from University of Michigan
Intro to AR/VR/MR/XR: Technologies, Applications & Issues from University of Michigan
Introduction to Parallel Programming with CUDA from Johns Hopkins University
Introduction to Object-Oriented Programming with Java II: Object-Oriented Programming and Algorithms from Georgia Institute of Technology
Databases: OLAP and Recursion from Stanford University
Minecraft, Coding and Teaching from University of California, San Diego
Database Architecture, Scale, and NoSQL with Elasticsearch from University of Michigan
Cloud Systems Software from Georgia Institute of Technology
Introduction à la science des données sociales avec R from Université de Montréal
Practical Python for AI Coding 2 from Korea Advanced Institute of Science and Technology
Programming Reactive Systems from École Polytechnique Fédérale de Lausanne
Projet de programmation (en Java) from École Polytechnique Fédérale de Lausanne
Getting Started with Data Visualization in R from Johns Hopkins University
Cloud Computing Project from University of Illinois at Urbana-Champaign
Python Classes and Inheritance from University of Michigan
Advanced Data Visualization with R from Johns Hopkins University
Django Features and Libraries from University of Michigan
Programação para todos (Conceitos básicos de Python) from University of Michigan
Effective Programming in Scala from École Polytechnique Fédérale de Lausanne
Building Web Applications in Django from University of Michigan
Developing AR/VR/MR/XR Apps with WebXR, Unity & Unreal from University of Michigan
Data Visualization in R with ggplot2 from Johns Hopkins University
Programming Reactive Systems (Scala 2 version) from École Polytechnique Fédérale de Lausanne
Web Application Technologies and Django from University of Michigan
Coding the Static Restaurant Site from Johns Hopkins University
Importing Data in the Tidyverse from Johns Hopkins University
Functional Programming Principles in Scala (Scala 2 version) from École Polytechnique Fédérale de Lausanne
Visualizing Data in the Tidyverse from Johns Hopkins University
CUDA Advanced Libraries from Johns Hopkins University
Functional Program Design in Scala (Scala 2 version) from École Polytechnique Fédérale de Lausanne
面向对象技术高级课程（The Advanced Object-Oriented Technology） from Peking University
Modeling Data in the Tidyverse from Johns Hopkins University
Introduction to the Tidyverse from Johns Hopkins University
CUDA at Scale for the Enterprise from Johns Hopkins University
Introduction to Internationalization and Localization from University of Washington
Programming Reactive Systems from École Polytechnique Fédérale de Lausanne
Parallel programming (Scala 2 version) from École Polytechnique Fédérale de Lausanne
Publishing Visualizations in R with Shiny and flexdashboard from Johns Hopkins University
Wrangling Data in the Tidyverse from Johns Hopkins University
مقدمة عن لغة HTML5 from University of Michigan
Django Features and Libraries from University of Michigan
Functional Programming in Scala Capstone from École Polytechnique Fédérale de Lausanne
Estruturas de dados Python from University of Michigan
Uso de bancos de dados com Python from University of Michigan
MATLAB et Octave pour débutants from École Polytechnique Fédérale de Lausanne
C++程序设计 from Peking University
Estructuras de Datos con Python from University of Michigan
Introducción al HTML5 from University of Michigan
Java程序设计 from Peking University
Programación para todos (Introducción a Python) from University of Michigan
مقدمة عن CSS3 from University of Michigan
Einführung in MATLAB from Technische Universität München (Technical University of Munich)
C#程序设计 from Peking University
Python بُنى بيانات from University of Michigan
برمج بنفسك! مقدمة حول البرمجة from University of Edinburgh
Virtual Reality from University of Illinois at Urbana-Champaign
Uso de bases de datos con Python from University of Michigan
تنميط متقدم بتصميم سريع الاستجابة from University of Michigan
程序设计基础 from Peking University
Python استخدام قواعد البيانات مع from University of Michigan
Введение в HTML5 from University of Michigan
Uso de Python para Acceder a Datos Web from University of Michigan
CS193p – Developing Apps for iOS from Stanford University
CS193p iPhone Application Development Spring 2020 from Stanford University
Estructuras de datos de Python from University of Michigan
C程序设计进阶 from Peking University
Stanford Seminar – Extended Reality for Everybody, Michael Nebeling from Stanford University
Stanford Seminar-Stories from CoCoLab: Probabilistic Programs, Cognative Modeling, & Smart Web Pages from Stanford University
游戏策划与设计 from Fudan University
基于Unity引擎的游戏开发基础 from Fudan University
Stanford Seminar – Accessible Virtual Reality for People with Limited Mobility from Stanford University
Stanford Seminar – How to Design Addictive Games from Stanford University
CS 241: System Programming from University of Illinois at Urbana-Champaign
Learn Computer Science Online from University of Illinois at Urbana-Champaign
Haskell: Lecture notes and assignments from University of Pennsylvania
15-445/645 Intro to Database Systems (Fall 2017) from Carnegie Mellon University
15-721 Advanced Database Systems (Spring 2016) from Carnegie Mellon University
游戏产业概论 from Fudan University
网络游戏设计与开发毕业项目 from Fudan University
Stanford Seminar – Programing Should Be More Than Coding from Stanford University
Stanford Seminar – Concatenative Programming: From Ivory to Metal from Stanford University
Работа с базами данных в Python from University of Michigan
Programming languages the fundamental tools of the computer age from University of Melbourne
Ciencia de Datos: Fundamentos de R from Harvard University
15-721 Advanced Database Systems (Spring 2020) from Carnegie Mellon University
15-445/645 Intro to Database Systems (Fall 2018) from Carnegie Mellon University
15-721 Advanced Database Systems (Spring 2018) from Carnegie Mellon University
软件工程 from Peking University
Stanford Seminar – Mind Your State for Your State of Mind from Stanford University
Использование языка Python для доступа к веб-данным from University of Michigan
Python에서 데이터베이스 사용하기 from University of Michigan
Missing Semester IAP 2020 from Massachusetts Institute of Technology
15-445/645 Intro to Database Systems (Fall 2021) from Carnegie Mellon University
Hardware Accelerated Database Lectures (Fall 2018) from Carnegie Mellon University
Time Series Database Lectures (Fall 2017) from Carnegie Mellon University
The Databaseology Lectures (Fall 2015) from Carnegie Mellon University
Stanford Seminar – Making Teamwork an Objective Discipline – Sid Sijbrandij CEO & Chairman of GitLab from Stanford University
Stanford Seminar – KUtrace 2020 from Stanford University
Stanford Seminar – Wearing a VR Headset While Driving to Improve Vehicle Safety from Stanford University
Структуры данных Python from University of Michigan
R 프로그래밍 from Johns Hopkins University
Structure and Interpretation of Computer Programs from Massachusetts Institute of Technology
15-445/645 Intro to Database Systems (Fall 2019) from Carnegie Mellon University
PACS & AI – From Integration to Cloud from Yale University
VR/AR in IR: Mixed Reality in Medicine from Yale University
基于Unity引擎的游戏开发进阶 from Fudan University
Jeremy Bailenson: Your Mind on the Metaverse from Stanford University
Stanford Seminar – #TechFail: From Intersectional (In)Accessibility to Inclusive Design from Stanford University
Stanford Seminar – Understanding the Utility of Haptic Feedback in Telerobotic Devices from Stanford University
Stanford Seminar: I forgot, I invented hypertext – Ted Nelson from Stanford University
Stanford Seminar – Making the Invisible Visible: Observing Complex Software Dynamics from Stanford University
Stanford Seminar – Edge Computing and the Evolution of AR/VR (panel discussion) from Stanford University
Stanford Seminar – The Future of Edge Computing from an International Perspective from Stanford University
Stanford Seminar: Virtual & Mixed Reality for Security of Critical City-Scale Cyber-Physical Systems from Stanford University
Stanford Seminar – Robosion: Software Platform for Lifelike Humanoids from Stanford University
Stanford Seminar – Rocket: Securing the Web at Compile-time from Stanford University
Stanford Seminar – Graph Analysis of Russian Twitter Trolls Using Neo4j from Stanford University
Stanford Seminar – An Alternative to the American way of Innovation from Stanford University
Stanford Seminar: PyWren – Pushing Microservices to Teraflops from Stanford University
Stanford Seminar: Living in Information Everywhere from Stanford University
Stanford Seminar – From Flat to Phantasmal from Stanford University
Computer Language Engineering (SMA 5502) from Massachusetts Institute of Technology
Internet Technology in Local and Global Communities from Massachusetts Institute of Technology
빅 데이터 모델링 및 관리 시스템 from University of California, San Diego
17. Paul’s Disciples from Yale University
LaTeX course from University of Amsterdam
JAVA程序设计进阶 from Tsinghua University

Computer Science (736)

Information Systems Auditing, Controls and Assurance from The Hong Kong University of Science and Technology ★★★★★(686)
Harvard CS50 – Full Computer Science University Course from Harvard University ★★★★★(615)
CS50’s Introduction to Computer Science from Harvard University ★★★★★(183)
Introduction to Computer Science and Programming Using Python from Massachusetts Institute of Technology ★★★★☆(130)
Artificial Intelligence from Massachusetts Institute of Technology ★★★★★(76)
Divide and Conquer, Sorting and Searching, and Randomized Algorithms from Stanford University ★★★★★(68)
Functional Programming Principles in Scala from École Polytechnique Fédérale de Lausanne ★★★★★(65)
Algorithms, Part I from Princeton University ★★★★☆(62)
Cryptography I from Stanford University ★★★★★(53)
Internet History, Technology, and Security from University of Michigan ★★★★★(41)
Introduction to Electrical Engineering and Computer Science I from Massachusetts Institute of Technology ★★★★★(41)
Machine Learning Foundations: A Case Study Approach from University of Washington ★★★★☆(40)
Introduction to Artificial Intelligence from Stanford University ★★★★☆(31)
Machine Learning with Python: from Linear Models to Deep Learning. from Massachusetts Institute of Technology ★★★☆☆(28)
Practical Machine Learning from Johns Hopkins University ★★★☆☆(27)
Data Structures & Algorithms I: ArrayLists, LinkedLists, Stacks and Queues from Georgia Institute of Technology ★★★★★(25)
Algorithmic Toolbox from University of California, San Diego ★★★★☆(23)
CS50’s Introduction to Artificial Intelligence with Python from Harvard University ★★★★★(22)
Algorithms, Part II from Princeton University ★★★★★(21)
Cloud Computing Concepts, Part 1 from University of Illinois at Urbana-Champaign ★★★☆☆(21)
Introduction to Algorithms from Massachusetts Institute of Technology ★★★★★(21)
Machine Learning: Regression from University of Washington ★★★★★(20)
Introduction to Machine Learning Course from Stanford University ★★★★☆(20)
Introduction to Logic from Stanford University ★★★☆☆(20)
Automata Theory from Stanford University ★★★★☆(20)
Bitcoin and Cryptocurrency Technologies from Princeton University ★★★★★(19)
Computer Science 101 from Stanford University ★★★★☆(19)
CS50’s Computer Science for Business Professionals from Harvard University ★★★★★(18)
Probabilistic Graphical Models 1: Representation from Stanford University ★★★★☆(18)
Cryptocurrency Engineering and Design from Massachusetts Institute of Technology ★★★★★(18)
Neural Networks and Deep Learning from DeepLearning.AI ★★★★★(16)
Data Structures from University of California, San Diego ★★★★☆(16)
CS50’s Understanding Technology from Harvard University ★★★★☆(15)
How to Code: Simple Data from The University of British Columbia ★★★★☆(15)
Design of Computer Programs from Stanford University ★★★★☆(14)
Machine Learning With Big Data from University of California, San Diego ★★☆☆☆(14)
Unix Tools: Data, Software and Production Engineering from Delft University of Technology ★★★★★(13)
Human-Computer Interaction I: Fundamentals & Design Principles from Georgia Institute of Technology ★★★★★(13)
Text Retrieval and Search Engines from University of Illinois at Urbana-Champaign ★★★☆☆(13)
Discrete Optimization from University of Melbourne ★★★★☆(12)
Introduction to Deep Learning from Massachusetts Institute of Technology ★★★★★(12)
Learning from Data (Introductory Machine Learning course) from California Institute of Technology ★★★★★(10)
Machine Learning: Classification from University of Washington ★★★★★(9)
Mathematics for Machine Learning: Multivariate Calculus from Imperial College London ★★★★★(9)
Cloud Computing Applications, Part 1: Cloud Systems and Infrastructure from University of Illinois at Urbana-Champaign ★★★☆☆(9)
Hardware Security from University of Maryland, College Park ★★★☆☆(9)
Convolutional Neural Networks from DeepLearning.AI ★★★★★(8)
CS50’s Computer Science for Lawyers from Harvard University ★★★★★(8)
Software Defined Networking from Georgia Institute of Technology ★★★★☆(8)
Information and Communication Technology (ICT) Accessibility from Georgia Institute of Technology ★★★★☆(8)
Reinforcement Learning from Brown University ★★★☆☆(8)
Usable Security from University of Maryland, College Park ★★★☆☆(8)
Machine Learning Fundamentals from University of California, San Diego ★★★★☆(8)
Machine Learning from Georgia Institute of Technology ★★★★☆(7)
Introduction to Computer Vision from Georgia Institute of Technology ★★★★★(7)
Cryptography from University of Maryland, College Park ★★★★☆(7)
Data Structures & Algorithms II: Binary Trees, Heaps, SkipLists and HashMaps from Georgia Institute of Technology ★★★★★(7)
Interactive Computer Graphics from University of Tokyo ★★★☆☆(7)
Guided Tour of Machine Learning in Finance from New York University (NYU) ★☆☆☆☆(7)
Performance Engineering of Software Systems from Massachusetts Institute of Technology ★★★★★(7)
Advanced Operating Systems from Georgia Institute of Technology ★★★★★(6)
Introduction to Computer Architecture from Carnegie Mellon University ★★★★★(6)
Cloud Computing Concepts: Part 2 from University of Illinois at Urbana-Champaign ★★★★★(6)
Computer Graphics from University of California, San Diego ★★★★☆(6)
Analysis of Algorithms from Princeton University ★★★★☆(6)
Data Structures and Performance from University of California, San Diego ★★★★☆(6)
Computer Networking from Georgia Institute of Technology ★★★★☆(6)
Structuring Machine Learning Projects from DeepLearning.AI ★★★★☆(6)
Computer Architecture from Princeton University ★★★★☆(6)
Applied Machine Learning in Python from University of Michigan ★★★★☆(6)
Internet of Things: How did we get here? from University of California, San Diego ★★☆☆☆(6)
Machine Learning for Healthcare from Massachusetts Institute of Technology ★★★★★(6)
Data Structures: An Active Learning Approach from University of California, San Diego ★★★★★(5)
Machine Learning: Clustering & Retrieval from University of Washington ★★★★★(5)
How to Code: Complex Data from The University of British Columbia ★★★★★(5)
Cloud Networking from University of Illinois at Urbana-Champaign ★★★★☆(5)
Data Structures & Algorithms III: AVL and 2-4 Trees, Divide and Conquer Algorithms from Georgia Institute of Technology ★★★★★(5)
Internet of Things: Setting Up Your DragonBoard™ Development Platform from University of California, San Diego ★★★☆☆(5)
Introduction to Computer Science and Programming in Python from Massachusetts Institute of Technology ★★★★★(5)
Convolutional Neural Networks for Visual Recognition (Spring 2017) from Stanford University ★★★★☆(5)
AI for Clinical Trials and Precision Medicine | Ruishan Liu from Stanford University ★★★★★(5)
Electrical and Computer Engineering – ECE 252 from University of Waterloo ★★★★☆(5)
Sequence Models from DeepLearning.AI ★★★★★(4)
Human-Computer Interaction II: Cognition, Context & Culture from Georgia Institute of Technology ★★★★★(4)
Programming Languages, Part B from University of Washington ★★★★☆(4)
The Unix Workbench from Johns Hopkins University ★★★★★(4)
Data Structures & Algorithms IV: Pattern Matching, Dijkstra’s, MST, and Dynamic Programming Algorithms from Georgia Institute of Technology ★★★★★(4)
6.S191: Introduction to Deep Learning from Massachusetts Institute of Technology ★★★★☆(4)
Algorithms on Strings from University of California, San Diego ★★★☆☆(4)
MIT 6.824 Distributed Systems (Spring 2020) from Massachusetts Institute of Technology ★★★★★(4)
Distributed Systems from University of Cambridge ★★★★★(4)
Language, Proof and Logic from Stanford University ★★★★☆(3)
Software Architecture & Design from Georgia Institute of Technology ★★★★★(3)
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization from DeepLearning.AI ★★★★★(3)
Machine Learning: Unsupervised Learning from Brown University ★★★☆☆(3)
Leading Change in Health Informatics from Johns Hopkins University ★★★★★(3)
Robotics: Perception from University of Pennsylvania ★★★☆☆(3)
Internet of Things: Communication Technologies from University of California, San Diego ★★★☆☆(3)
Object-Oriented Data Structures in C++ from University of Illinois at Urbana-Champaign ★★★★☆(3)
Probabilistic Graphical Models 2: Inference from Stanford University ★★★★☆(3)
Algorithms on Graphs from University of California, San Diego ★★★★☆(3)
Mathematics for Machine Learning: PCA from Imperial College London ★★☆☆☆(3)
Algorithmic Design and Techniques from University of California, San Diego ★★★★☆(3)
Advanced Algorithms (COMPSCI 224) from Harvard University ★★★★★(3)
Stanford Webinar – Using Electronic Health Records for Better Care from Stanford University ★★★★★(3)
Stanford Seminar – Practical Blockchain Applications – Steven Pu from Stanford University ★★★★★(3)
Stanford Seminar – Security and the Software Defined Network from Stanford University ★★★★☆(3)
Introduction to Operating Systems from Georgia Institute of Technology ★★★★★(2)
HI-FIVE: Health Informatics For Innovation, Value & Enrichment (Administrative/IT Perspective) from Columbia University ★★★★★(2)
Programming Languages, Part C from University of Washington ★★★★☆(2)
Human-Computer Interaction IV: Evaluation, Agile Methods & Beyond from Georgia Institute of Technology ★★★★★(2)
Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud from University of Illinois at Urbana-Champaign ★★★☆☆(2)
Advanced Data Structures in Java from University of California, San Diego ★★★★☆(2)
Software Engineering: Introduction from The University of British Columbia ★★★☆☆(2)
Knowledge-Based AI: Cognitive Systems from Georgia Institute of Technology ★★★☆☆(2)
Creative Audio Programming on the Raspberry Pi from University of New South Wales ★★★★★(2)
6.S094: Deep Learning for Self-Driving Cars from Massachusetts Institute of Technology ★★★★☆(2)
Fundamentals of Machine Learning in Finance from New York University (NYU) ★★☆☆☆(2)
Reinforcement Learning in Finance from New York University (NYU) ★☆☆☆☆(2)
The Critical Role of IT Support Staff in Healthcare from Johns Hopkins University ★★★★☆(2)
Introduction to Algorithms (SMA 5503) from Massachusetts Institute of Technology ★★★★★(2)
Stanford CS547 – Human-Computer Interaction Seminar Series from Stanford University ★★★★★(2)
Stanford Seminar – How to Compute with Schrödinger’s Cat: An Introduction to Quantum Computing from Stanford University ★★★★☆(2)
Deep Learning Lecture Series from University College London ★★★★★(2)
Unsupervised Biomedical Image Segmentation using Hyperbolic Representations | Jeffrey Gu from Stanford University ★★★★☆(2)
Computability, Complexity & Algorithms from Georgia Institute of Technology ★★★★★(1)
Computer Science: Programming with a Purpose from Princeton University ★★★★★(1)
High Performance Computer Architecture from Georgia Institute of Technology ★★★★★(1)
IoT Devices from University of Illinois at Urbana-Champaign ★★★★★(1)
Deep Learning for Natural Language Processing from University of Oxford ★★★★★(1)
Natural Language Processing: Foundations from National University of Singapore ★★★★★(1)
Health Informatics: A Current and Historical Perspective from Georgia Institute of Technology ★★★★★(1)
Introduction to Self-Driving Cars from University of Toronto ★★★★★(1)
HI-FIVE: Health Informatics For Innovation, Value & Enrichment (Clinical Perspective) from Columbia University ★★★★☆(1)
Compilers from Stanford University ★★★★★(1)
Human-Computer Interaction III: Ethics, Needfinding & Prototyping from Georgia Institute of Technology ★★★★★(1)
Python Project: Software Engineering and Image Manipulation from University of Michigan ★★★☆☆(1)
Graph Search, Shortest Paths, and Data Structures from Stanford University ★☆☆☆☆(1)
Advanced Algorithms and Complexity from University of California, San Diego ★★★☆☆(1)
Foundations of Healthcare Systems Engineering from Johns Hopkins University ★★★★★(1)
Blockchains, Tokens, and The Decentralized Future from University of Illinois at Urbana-Champaign ★★★☆☆(1)
Data Structures and Algorithm Design Part II | 数据结构与算法设计(下) from Tsinghua University ★★★★★(1)
The Quantum Internet and Quantum Computers: How Will They Change the World? from Delft University of Technology ★★☆☆☆(1)
Health Information Technology Fundamentals from Johns Hopkins University ★★☆☆☆(1)
Software Construction: Object-Oriented Design from The University of British Columbia ★★☆☆☆(1)
Computational Thinking for Modeling and Simulation from Massachusetts Institute of Technology ★★☆☆☆(1)
Software Construction: Data Abstraction from The University of British Columbia ★★★☆☆(1)
Overview of Advanced Methods of Reinforcement Learning in Finance from New York University (NYU) ★☆☆☆☆(1)
Nature, in Code: Biology in JavaScript from École Polytechnique Fédérale de Lausanne ★★★☆☆(1)
Introduction to Algorithms from Massachusetts Institute of Technology ★★★★☆(1)
Artificial Intelligence for Breast Cancer Detection from Johns Hopkins University ★★★★☆(1)
The AI Awakening: Implications for the Economy and Society from Stanford University ★★★★☆(1)
Introduction to Computational Thinking and Data Science from Massachusetts Institute of Technology ★★★★★(1)
Introduction to Computer Science and Programming (Fall 2008) from Massachusetts Institute of Technology ★★★★★(1)
Deep Learning for Computer Vision from University of Michigan ★★★★★(1)
Design and Analysis of Algorithms from Massachusetts Institute of Technology ★★★★★(1)
Beyond Cryptocurrency: Blockchain for the Real World from Stanford University ★★★★☆(1)
Stanford Seminar – How Not to Generate Random Numbers from Stanford University ★★★★★(1)
Stanford Seminar – Building the Smartest and Open Virtual Assistant to Protect Privacy – Monica Lam from Stanford University ★★★★★(1)
Stanford Webinar – How ChatGPT and Generative AI Will Shape the Future of Work from Stanford University ★☆☆☆☆(1)
EI Seminar – Grey Yang – Tuning GPT-3 on a Single GPU via Zero-Shot Hyperparameter Transfer from Massachusetts Institute of Technology ★★★★★(1)
Stanford Webinar: IOT – From Smart Sensors to Smart Networks from Stanford University ★★★★★(1)
Stanford Seminar – Rethinking the AI-UX Boundary for Designing Human-AI Experiences from Stanford University ★★★★★(1)
Generalization and Personalization in Federated Learning | Karan Singhal from Stanford University ★★★★★(1)
MedAI: Graph-based modeling in computational pathology | Siyi Tang from Stanford University ★★★★★(1)
EI Seminar – Martin Riedmiller – Learning Controllers – From Engineering to AGI from Massachusetts Institute of Technology ★★★★☆(1)
Stanford Seminar – Toward Scalable Autonomy – Aleksandra Faust from Stanford University ★★★★☆(1)
Regulatory evaluation of image processing software devices from Yale University ★★★★★(1)
Stanford Seminar: Self-Driving Cars for Everyone from Stanford University ★★★★★(1)
MIT EI Seminar – Russ Tedrake – Feedback control from pixels from Massachusetts Institute of Technology ★★★☆☆(1)
MIT 6.S191 (2021): Deep Generative Modeling from Alexander Amini ★★★★★(1)
Wireless Above 100GHz from New York University (NYU) ★★★☆☆(1)
Stanford Seminar: Google’s Multilingual Neural Machine Translation System from Stanford University ★★★★★(1)
ChatGPT Teach-Out from University of Michigan
Computational Thinking for Problem Solving from University of Pennsylvania
Algorithms: Design and Analysis, Part 1 from Stanford University
Generative AI Teach-Out from University of Michigan
Computer Science: Algorithms, Theory, and Machines from Princeton University
Cyber-Physical Systems Design & Analysis from Georgia Institute of Technology
Decision Making and Reinforcement Learning from Columbia University
HI-FIVE: Health Informatics For Innovation, Value & Enrichment (Social/Peer Perspective) from Columbia University
Introduction to Quantum Computing from The University of British Columbia
Artificial Intelligence from Georgia Institute of Technology
Hacker Tools from Massachusetts Institute of Technology
Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming from Stanford University
Internet of Things: Multimedia Technologies from University of California, San Diego
Programming & Data Structures from Columbia University
Human-Computer Interaction from Georgia Institute of Technology
Software Defined Networking from The University of Chicago
Number Theory and Cryptography from University of California, San Diego
AI skills: Introduction to Unsupervised, Deep and Reinforcement Learning from Delft University of Technology
Internet of Things: Sensing and Actuation From Devices from University of California, San Diego
Machine Learning for Semiconductor Quantum Devices from Delft University of Technology
Getting started with TensorFlow 2 from Imperial College London
High Performance Computing from Georgia Institute of Technology
The Data Science of Health Informatics from Johns Hopkins University
Fundamentals of Machine Learning for Healthcare from Stanford University
IoT Networking from University of Illinois at Urbana-Champaign
Machine Learning and AI with Python from Harvard University
Introduction to Blockchain Technology and Applications from University College London
Algorithms: Design and Analysis, Part 2 from Stanford University
Development and Applications of Germanium Quantum Technologies from Delft University of Technology
Ethics in AI Design from Delft University of Technology
Introduction to Clinical Data from Stanford University
Computer Applications of Artificial Intelligence and e-Construction from Purdue University
Health Informatics: Data and Interoperability Standards from Georgia Institute of Technology
Generative AI: Fundamentals, Applications, and Challenges from University of Michigan
Artificial Intelligence Essentials from University of Pennsylvania
AI skills for Engineers: Supervised Machine Learning from Delft University of Technology
Evaluations of AI Applications in Healthcare from Stanford University
AI Applications in People Management from University of Pennsylvania
AI in Healthcare Capstone from Stanford University
Machine Learning and Human Learning from University of Illinois at Urbana-Champaign
Camera and Imaging from Columbia University
Deep Learning Methods for Healthcare from University of Illinois at Urbana-Champaign
The Social and Technical Context of Health Informatics from Johns Hopkins University
Introduction to Computational Science and Engineering from Massachusetts Institute of Technology
Machine Learning: Concepts and Applications from The University of Chicago
Generative AI: Impact on Business and Society from University of Michigan
Input and Interaction from University of California, San Diego
IoT Communications from University of Illinois at Urbana-Champaign
Assessment Design with AI from Georgia Institute of Technology
Introduction to Machine Learning in Sports Analytics from University of Michigan
AI Strategy and Governance from University of Pennsylvania
AI for Teacher Assistance from Georgia Institute of Technology
Search Engines for Web and Enterprise Data from The Hong Kong University of Science and Technology
Introduction to Scientific Machine Learning from Purdue University
Generative AI: Labor and the Future of Work from University of Michigan
Complex Problem Structuring: a Socio-Technical Perspective from Delft University of Technology
Advanced Deep Learning Methods for Healthcare from University of Illinois at Urbana-Champaign
Shortest Paths Revisited, NP-Complete Problems and What To Do About Them from Stanford University
Introduction to Deep Learning from Purdue University
Statistical Machine Learning from Carnegie Mellon University
Creative Coding for Designers Using Python from University of Michigan
Network Function Virtualization from Georgia Institute of Technology
Blockchain Scalability and its Foundations in Distributed Systems from The University of Sydney
AI Applications in Marketing and Finance from University of Pennsylvania
Moving to the Cloud from University of Melbourne
Image Processing and Analysis for Life Scientists from École Polytechnique Fédérale de Lausanne
Compilers: Theory and Practice from Georgia Institute of Technology
Chatbots for Instruction from Georgia Institute of Technology
Probabilistic Graphical Models 3: Learning from Stanford University
Accessible Gamification from Georgia Institute of Technology
Applied Quantum Computing I: Fundamentals from Purdue University
Android Graphics with OpenGL ES from Imperial College London
Machine Learning from Georgia Institute of Technology
AI Materials from Korea Advanced Institute of Science and Technology
Industrial Internet of Things (IIoT) from University of Michigan
Building Autonomous AI from University of Washington
Unsupervised Learning, Recommenders, Reinforcement Learning from DeepLearning.AI
Health Informatics: The Cutting Edge from Georgia Institute of Technology
Machine Learning for Accounting with Python from University of Illinois at Urbana-Champaign
Data Structures Fundamentals from University of California, San Diego
IoT Systems and Industrial Applications with Design Thinking from École Polytechnique Fédérale de Lausanne
Python Fundamentals for Designers from University of Michigan
Advanced Learning Algorithms from DeepLearning.AI
Customising your models with TensorFlow 2 from Imperial College London
Machine Teaching for Autonomous AI from University of Washington
Applications of Machine Learning in Plant Science from Cornell University
Machine Learning Use Cases in Finance from Université de Montréal
AI Fundamentals for Non-Data Scientists from University of Pennsylvania
Introduction to Graduate Algorithms from Georgia Institute of Technology
Introduction to Quantum Computing for Everyone from The University of Chicago
Visual Perception for Self-Driving Cars from University of Toronto
Computer Vision for Embedded Systems from Purdue University
Visual Perception from Columbia University
Computer Graphics from University of California, San Diego
Deep Learning Essentials from Université de Montréal
Computing for Cancer Informatics from Johns Hopkins University
The Outcomes and Interventions of Health Informatics from Johns Hopkins University
Internet of Things V2: Setting up and Using Cloud Services from University of California, San Diego
Introduction to Quantum Computing for Everyone 2 from The University of Chicago
Solving Algorithms for Discrete Optimization from University of Melbourne
Communications and High-Speed Signals with Raspberry Pi from Johns Hopkins University
3D Reconstruction – Multiple Viewpoints from Columbia University
Data Structures for Designers Using Python from University of Michigan
Designing Hardware for Raspberry Pi Projects from Johns Hopkins University
IoT Cloud from University of Illinois at Urbana-Champaign
Machine Learning Essentials from University of Pennsylvania
Fundamentals of Quantum Information from Delft University of Technology
Beginning Custom Projects with Raspberry Pi from Johns Hopkins University
Motion Planning for Self-Driving Cars from University of Toronto
Fundamentals of TinyML from Harvard University
AI in Healthcare. Hype or Help? from KU Leuven University
Introduction to Digital Health Entrepreneurship from Johns Hopkins University
Graph Algorithms from University of California, San Diego
Leadership and Management for PM Practitioners in IT from University of Washington
MLOps for Scaling TinyML from Harvard University
Applied Quantum Computing III: Algorithm and Software from Purdue University
Self-Driving Cars with Duckietown from ETH Zurich
Deploying TinyML from Harvard University
3D Reconstruction – Single Viewpoint from Columbia University
State Estimation and Localization for Self-Driving Cars from University of Toronto
Basic Modeling for Discrete Optimization from University of Melbourne
Introduction to Quantum Information from Korea Advanced Institute of Science and Technology
Data Augmented Technology Assisted Medical Decision Making from University of Michigan
Data Analytics Foundations for Accountancy II from University of Illinois at Urbana-Champaign
Mathematics for Computer Science from Massachusetts Institute of Technology
Introduction à l’éthique de l’IA from Université de Montréal
Information Extraction from Free Text Data in Health from University of Michigan
Supervised Machine Learning: Regression and Classification from DeepLearning.AI
Internet of Things V2: DragonBoard™ bring up and community ecosystem from University of California, San Diego
Quantum Detectors and Sensors from Purdue University
Applied Quantum Computing II: Hardware from Purdue University
Generative AI Essentials: Overview and Impact from University of Michigan
Data Structures and Algorithm Design Part I | 数据结构与算法设计(上) from Tsinghua University
Designing Autonomous AI from University of Washington
Applications of TinyML from Harvard University
Quantum Computer Systems Design I: Intro to Quantum Computation and Programming from The University of Chicago
Logistic Regression and Prediction for Health Data from University of Michigan
Design and Implementation of Digital Health Interventions from Imperial College London
Introduction to Concurrent Programming with GPUs from Johns Hopkins University
Using Sensors With Your Raspberry Pi from Johns Hopkins University
Advanced Modeling for Discrete Optimization from University of Melbourne
Boltzmann Law: Physics to Computing from Purdue University
Operations and Patient Safety for Healthcare IT Staff from Johns Hopkins University
Probabilistic Deep Learning with TensorFlow 2 from Imperial College London
Internet of Things Capstone: Build a Mobile Surveillance System from University of California, San Diego
Modern Distributed Systems from Delft University of Technology
Culminating Project in Health Informatics from Johns Hopkins University
计算机辅助翻译原理与实践 Principles and Practice of Computer-Aided Translation from Peking University
Introduction to CSS3 from Johns Hopkins University
Techniques d’intelligence artificielle : des fondements aux applications from Université de Montréal
Portfolio Management, Governance, & the PMO from University of Washington
iLabX – The Internet Masterclass from Technische Universität München (Technical University of Munich)
Quantum Computer Systems Design II: Principles of Quantum Architecture from The University of Chicago
Quantum Communication and the Quantum Network Explorer from Delft University of Technology
Supercomputing from Partnership for Advanced Computing in Europe
Explore Trading and Lending in Decentralized Finance from University of Toronto
Features and Boundaries from Columbia University
Introduction à l’apprentissage profond from Université de Montréal
Machine Learning Algorithms with R in Business Analytics from University of Illinois at Urbana-Champaign
Quantum Computer Systems Design III: Working with Noisy Systems from The University of Chicago
Emerging Technology, Disruption, and AI from University of Illinois at Urbana-Champaign
Internet of Things Capstone V2: Build a Mobile Surveillance System from University of California, San Diego
Bias and Discrimination in AI from Université de Montréal
Introduction to Computer Science and Programming (Spring 2011) from Massachusetts Institute of Technology
Ordered Data Structures from University of Illinois at Urbana-Champaign
The Society of Mind from Massachusetts Institute of Technology
GT – Refresher – Advanced OS from Georgia Institute of Technology
Data Structures and Algorithms (I) from Tsinghua University
Llama for Python Programmers from University of Michigan
操作系统原理（Operating Systems） from Peking University
String Processing and Pattern Matching Algorithms from University of California, San Diego
算法设计与分析 Design and Analysis of Algorithms from Peking University
Biais et discrimination en IA from Université de Montréal
Understanding the World Through Data – from Massachusetts Institute of Technology
Leveraging Generative AI for Social Impact Organizations from University of Michigan
Data Structures and Algorithms (III) from Tsinghua University
Advanced Data Structures from Massachusetts Institute of Technology
NP-Complete Problems from University of California, San Diego
Data Structures and Algorithms (IV) from Tsinghua University
Unordered Data Structures from University of Illinois at Urbana-Champaign
Blockchain Technology and the Future of FinTech from University of Toronto
Data Structures and Algorithms (II) from Tsinghua University
L’essentiel de l’apprentissage profond from Université de Montréal
AI in Practice: Preparing for AI from Delft University of Technology
Deploying Machine Learning Models from University of California, San Diego
Big Data Machine Learning | 大数据机器学习 from Tsinghua University
MIT MAS.S62 Cryptocurrency Engineering and Design, Spring 2018 from Massachusetts Institute of Technology
Les coulisses des systèmes de recommandation from Université de Montréal
Dive Into the World of Blockchain: Principles, Mechanics, and Tokens from University of Toronto
LAFF – On Programming for Correctness from The University of Texas at Austin
Vision artificielle et exploitation intelligente des ressources naturelles from Université de Montréal
LAFF-On Programming for High Performance from The University of Texas at Austin
计算几何 | Computational Geometry from Tsinghua University
Optimization: principles and algorithms – Network and discrete optimization from École Polytechnique Fédérale de Lausanne
Recommender Systems: Behind the Screen from Université de Montréal
Decoding AI: A Deep Dive into AI Models and Predictions from University of Michigan
离散优化算法篇 Solving Algorithms for Discrete Optimization from The Chinese University of Hong Kong
Deep Learning in Life Sciences – Spring 2021 from Massachusetts Institute of Technology
Optimization: principles and algorithms – Unconstrained nonlinear optimization from École Polytechnique Fédérale de Lausanne
计算机组成 Computer Organization from Peking University
Navigating Decentralized Derivatives and Governance in Blockchain from University of Toronto
AI in Practice: Applying AI from Delft University of Technology
Introduction to Computational Thinking from Massachusetts Institute of Technology
数据结构基础 from Peking University
Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018 from Stanford University
算法基础 from Peking University
Stanford Webinar – How [You] Can Use ChatGPT to Increase Your Creative Output from Stanford University
高级数据结构与算法 from Peking University
MIT 6.01SC Introduction to EECS I from Massachusetts Institute of Technology
The Battlecode Programming Competition from Massachusetts Institute of Technology
Geometric Folding Algorithms: Linkages, Origami, Polyhedra from Massachusetts Institute of Technology
Programming for the Puzzled (January IAP 2018) from Massachusetts Institute of Technology
Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019 from Stanford University
Stanford CS105 – Introduction to Computers Full Course from Stanford University
Stanford Seminar – Software-Defined Networking at the Crossroads from Stanford University
CS25 I Stanford Seminar – Transformers in Language: The development of GPT Models including GPT3 from Stanford University
Algorithmic Lower Bounds: Fun with Hardness Proofs from Massachusetts Institute of Technology
操作系统与虚拟化安全 from Peking University
MIT 18.404J Theory of Computation, Fall 2020 from Massachusetts Institute of Technology
Stanford Seminar – Deep Learning for Medical Diagnoses from Stanford University
离散优化建模基础篇 Basic Modeling for Discrete Optimization from The Chinese University of Hong Kong
Multicore Programming Primer from Massachusetts Institute of Technology
离散优化建模高阶篇 Advanced Modeling for Discrete Optimization from The Chinese University of Hong Kong
Stanford Seminar – The FATE of AI Ethics, Anna Bethke from Stanford University
Machine Learning Course – CS 156 from California Institute of Technology
Artificial Intelligence Planning from University of Edinburgh
Stanford Seminar – Representation Learning for Autonomous Robots, Anima Anandkumar from Stanford University
Stanford Seminar – Deep Learning in Speech Recognition from Stanford University
算法设计与分析(高级) | Advanced Design and Analysis of Algorithms from Peking University
Stanford CS234: Reinforcement Learning | Winter 2019 from Stanford University
Stanford Seminar – Using Data for Increased Realism with Haptic Modeling and Devices from Stanford University
Stanford Seminar – Enabling NLP, Machine Learning, & Few-Shot Learning using Associative Processing from Stanford University
Stanford Seminar – Designing Assistive Technologies for Agency from Stanford University
Stanford Seminar – Creating Interfaces with Rich Physical Properties Through Digital Fabricationity from Stanford University
Stanford Seminar – Natural Language Processing for Production-Level Conversational Interfaces from Stanford University
Stanford Seminar – Software-centric Visible Light Communication for the Internet of Things from Stanford University
MIT EI Seminar – Phillip Isola – Emergent Intelligence: getting more out of agents than you bake in from Massachusetts Institute of Technology
MIT 6.S191: Reinforcement Learning from Alexander Amini
Stanford Seminar – Neural Networks on Chip Design from the User Perspective from Stanford University
Stanford Seminar – Training Classifiers with Natural Language Explanations from Stanford University
Stanford Seminar – Bugs in Crypto Implementations from Stanford University
Stanford Seminar – Can the brain do back-propagation? from Stanford University
Federated Hyperparameters Tuning: Challenges, Baselines & Connections | Mikhail Khodak from Stanford University
Frontiers of Medical AI – Therapeutics and Workflows | Andre Esteva from Stanford University
Алгоритмы, часть I from Princeton University
EI Seminar – Rob Fergus – Data Augmentation for Image-Based Reinforcement Learning from Massachusetts Institute of Technology
Stanford Seminar – Citadel of One: Individuality and the rise of the machines, Suzanne Sadedin from Stanford University
Weakly-supervised, large-scale computational pathology for diagnosis & prognosis | Max Lu from Stanford University
Rethinking Architecture Design for Data Heterogeneity in FL | Liangqiong Qu from Stanford University
EI Seminar – Recent papers in Embodied Intelligence from Massachusetts Institute of Technology
EI Seminar – Yejin Choi – Intuitive Reasoning as (Un)supervised Neural Generation from Massachusetts Institute of Technology
EI Seminar – Michael Carbin – The Lottery Ticket Hypothesis from Massachusetts Institute of Technology
MIT EI Seminar – Lerrel Pinto – Diverse data and efficient algorithms for robot learning from Massachusetts Institute of Technology
MIT 6.S191 (2020): Neurosymbolic AI from Alexander Amini
MIT 6.S191: Towards AI for 3D Content Creation from Alexander Amini
MIT 6.S191: AI in Healthcare from Alexander Amini
MIT Introduction to Deep Learning | 6.S191 from Alexander Amini
MIT 6.S191: Deep Generative Modeling from Alexander Amini
MIT 6.S191 (2018): Convolutional Neural Networks from Alexander Amini
MIT 6.S191 (2018): Beyond Deep Learning: Learning+Reasoning from Alexander Amini
MIT 6.S191 (2019): Visualization for Machine Learning (Google Brain) from Alexander Amini
Deep Maths – machine learning and mathematics from University of Oxford
The Creativity Code – Marcus du Sautoy from University of Oxford
CS154 Stanford – Introduction to the Theory of Computing from Stanford University
Stanford CS224W – Machine Learning with graphs- Fall 2019 from Stanford University
Stanford Seminar – Scalable Intelligent Systems Build and Deploy by 2025 from Stanford University
Stanford Seminar – Smart Physical Systems from the Standpoint of an AI Company from Stanford University
Deep Learning Methods for Electrocardiograms and Echocardiograms | Weston Hughes from Stanford University
Towards Generalist Imaging Using Multimodal Self-supervised Learning | Mars Huang from Stanford University
Few-shot Chest X-ray Diagnosis Using Clinical & Literature Images | Angshuman Paul from Stanford University
Ge Wang: The Artful Design of Interactive AI Systems from Stanford University
Stanford Seminar – Federated Learning in Medicine: Breaking Down Silos to Advance Medical Research from Stanford University
Stanford Seminar – Gender Disparities in Software Engineering from Stanford University
EI Seminar – Luke Zettlemoyer – Large Language Models: Will they keep getting bigger? from Massachusetts Institute of Technology
Daniel Wolpert – Computational principles underlying the learning of sensorimotor repertoires from Massachusetts Institute of Technology
EI Seminar – Jacob Andreas – Natural Language Explanations of Deep Networks from Massachusetts Institute of Technology
MIT 6.S191: AI Bias and Fairness from Alexander Amini
MIT 6.S191: Recurrent Neural Networks and Transformers from Alexander Amini
MIT 6.S191: Automatic Speech Recognition from Alexander Amini
MIT 6.S191 (2018): Deep Learning Limitations and New Frontiers from Alexander Amini
MIT 6.S191 (2019): Introduction to Deep Learning from Alexander Amini
MIT 6.S191 (2018): Introduction to Deep Learning from Alexander Amini
Improved ultrasound image formation—domain adaptation with no data from Yale University
Stanford CS330: Deep Multi-Task and Meta Learning from Stanford University
Stanford Seminar – Failures & Where to Find Them: Considering Safety as a Function of Structure from Stanford University
Stanford Seminar – Deep Learning for Symbolic Mathematics – Guillaume Lample & Francois Charton from Stanford University
Stanford Seminar – Lenia: Biology of Artificial Life, Bert Wang-Chak Chan from Stanford University
Stanford Seminar – Leela: a Semantic Intelligent Agent from Stanford University
Stanford Seminar – Safe passwords made easy to use from Stanford University
Stanford Seminar – An architect’s point of view on emerging technologies from Stanford University
Stanford Seminar – The Quest for Low Storage Latency Changes Everything from Stanford University
Stanford Seminar: The REX Neo Architecture: An energy efficient new processor architecture from Stanford University
Stanford Seminar: Concepts and Questions as Programs from Stanford University
Stanford Seminar – Dynamic Code Optimization and the NVIDIA Denver Processor from Stanford University
Stanford Seminar – Applying Theory to Practice (and Practice to Theory) from Stanford University
Stanford Seminar – Instruction Sets Should Be Free: The Case for RISC-V from Stanford University
Denoising diffusion models for denoising diffusion MRI | Tiange Xiang from Stanford University
Solana Larsen: Who has Power over AI? from Stanford University
Stanford Webinar – Web3 Considered: Possible Futures for Decentralization and Digital Ownership from Stanford University
Stanford Seminar – Rebooting the Internet from Stanford University
EI Seminar – Ruslan Salakhutdinov – Building Embodied Autonomous Agents from Massachusetts Institute of Technology
EI Seminar – Recent papers in Embodied Intelligence I from Massachusetts Institute of Technology
MIT 6.S191: Convolutional Neural Networks from Alexander Amini
MIT 6.S191 (2018): Sequence Modeling with Neural Networks from Alexander Amini
MIT 6.S191 (2018): Computer Vision Meets Social Networks from Alexander Amini
MIT 6.S191 (2019): Recurrent Neural Networks from Alexander Amini
MIT 6.S191 (2019): Convolutional Neural Networks from Alexander Amini
6.036 Introduction to Machine Learning from Massachusetts Institute of Technology
Stanford Seminar – Forecasting and Aligning AI, Jacob Steinhardt from Stanford University
Stanford Seminar – Erudite: Prototype System for Computational Intelligence from Stanford University
Stanford Seminar – Efficient and Resilient Systems in the Cognitive Era from Stanford University
Stanford Seminar – SMILE: Synchronized, Multi-sensory Integrated Learning Environment from Stanford University
Stanford Seminar – New Golden Age for Computer Architecture – John Hennessy from Stanford University
Stanford Seminar: Dialog Markets from Stanford University
Stanford Seminar: Time Traveling Hardware and Software Systems from Stanford University
Stanford Seminar: HPC Opportunities in Deep Learning – Greg Diamos, Baidu from Stanford University
Stanford Seminar – Instruction execution on the Mill CPU from Stanford University
Stanford Seminar – Mostly Missless Memory in the Mill CPU from Stanford University
Opioid Use Disorder Prediction Using AI and Existing Risk Models | Sajjad Fouladvand from Stanford University
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MedAI: Learning the Structure of EHR with Graph Convolutional Transformer | Edward Choi from Stanford University
Akshay Chaudhari: Data-Efficient AI for Accelerating MRI Acquisition from Stanford University
Chris Re: How Machine Learning is Changing Software from Stanford University
Against Ethical Robots with Ron Chrisley from Stanford University
Stanford Webinar – Artificial Intelligence for Business Leaders from Stanford University
AI and Law I Mariano-Florentino Cuéllar, President of the Carnegie Endowment for International Peace from Stanford University
Stanford Seminar – Accelerating ML Recommendation with over a Thousand RISC-V/Tensor Processors… from Stanford University
Stanford Webinar – Design & AI: Envisioning AI-augmented Products from Stanford University
Stanford CS229: Machine Learning | Summer 2019 | Lecture 8 – Kernel Methods & Support Vector Machine from Stanford University
Part Two! How [You] Can Use ChatGPT to Increase Your Creative Output from Stanford University
CS25 I Stanford Seminar – Transformers United: Neuroscience-Inspired Artificial Intelligence from Stanford University
Getting Robust: Securing Neural Networks against Adversarial Attacks from University of Melbourne
Can you trust a computer algorithm… how and why? from University of Melbourne
MIT EI Seminar – Pulkit Agrawal – The Task Specification Problem from Massachusetts Institute of Technology
MIT 6.S191 (2020): Machine Learning for Scent from Alexander Amini
MIT 6.S191 (2021): Introduction to Deep Learning from Alexander Amini
MIT 6.S191 (2021): Recurrent Neural Networks from Alexander Amini
MIT 6.S191 (2021): Convolutional Neural Networks from Alexander Amini
MIT 6.S191: Taming Dataset Bias via Domain Adaptation from Alexander Amini
MIT 6.S191 (2019): Deep Generative Modeling from Alexander Amini
MIT 6.S191 (2019): Biologically Inspired Neural Networks (IBM) from Alexander Amini
MIT 6.S191 (2020): Introduction to Deep Learning from Alexander Amini
MIT 6.S191 (2020): Recurrent Neural Networks from Alexander Amini
MIT 6.S191 (2020): Deep Generative Modeling from Alexander Amini
Modeling prior information to guide image reconstruction in radiation therapy from Yale University
程序开发项目实践 from Peking University
区块链和加密数字货币 from Tsinghua University
The Evolution of Public Key Cryptography from New York University (NYU)
Ideas for a Complex World – Anna Seigal from University of Oxford
GGP Course Videos from Stanford University
Stanford Seminar – Persistent and Unforgeable Watermarks for DeepNeural Networks from Stanford University
Stanford Seminar – MIPS Open, Wave Computing from Stanford University
Stanford Seminar – Computational memory: A stepping-stone to non-von Neumann computing? from Stanford University
Stanford Seminar – Distributed Perception and Learning Between Robots and the Cloud from Stanford University
Stanford Seminar: Computational Ecosystems from Stanford University
Explaining Model Decisions and Fixing Them through Human Feedback | Ramprasaath Selvaraju from Stanford University
Fairness in representation learning | Natalie Dullerud from Stanford University
Actionable Machine Learning for Tackling Distribution Shift | Huaxiu Yao from Stanford University
Generative models with domain knowledge for weakly supervised clustering | Laura Manduchi from Stanford University
Domain Adaptation with Invariant Representation Learning | Petar Stojanov from Stanford University
Efficiently Modeling Long Sequences with Structured State Spaces | Albert Gu from Stanford University
Optimizing for Interpretability in Deep Neural Networks | Mike Wu from Stanford University
GANs in Medical Image Synthesis, Translation, and Augmentation | Jason Jeong from Stanford University
Adversarial debiasing with partial learning – medical image studies | Ramon Correa from Stanford University
Assignment Control Plots for Causal Inference Study Design | Rocky Aikens from Stanford University
Observational Supervision for Medical Image Classification | Khaled Saab from Stanford University
Self-training/Weak Supervision + Untrained Neural Nets for MR Recon | Beliz Gunel from Stanford University
Mandoline – Model Evaluation under Distribution Shift | Mayee Chen from Stanford University
Style transfer augmentations for computational pathology | Rikiya Yamashita from Stanford University
MedAI: Self-supervision & Contrastive Frameworks: a vision-based review | Nandita Bhaskhar from Stanford University
Segmentation and Quantification of Breast Arterial Calcifications | Xiaoyuan Guo from Stanford University
HAI Weekly Seminar with Jeff Ding: The Rise and Fall of Great Technologies and Powers from Stanford University
Agrim Gupta: Towards Understanding and Building Embodied Intelligence from Stanford University
Jiajun Wu: Learning to See the Physical World from Stanford University
Stanford Seminar – Human-AI Interaction Under Societal Disagreement from Stanford University
Stanford Seminar – Why would we want a multi-agent system unstable from Stanford University
Stanford Lecture: Don Knuth – Twintrees, Baxter Permutations, and Floorplans (2022) from Stanford University
Stanford Webinar – Cryptocurrencies and Blockchains: The Science Behind the Technology, Dan Boneh from Stanford University
CS25 I Stanford Seminar 2022 – Self Attention and Non-parametric transformers (NPTs) from Stanford University
Stanford Seminar – Lessons from evaluating and debugging healthcare AI in deployment from Stanford University
Stanford Seminar – Safety (and Liveness!) of Robot Behaviors from Stanford University
Stanford Seminar – Is Google Search Is Dying? from Stanford University
Stanford Seminar – Interactive Imitation Learning: Planning Alongside Humans from Stanford University
Stanford Seminar – 4004 Microprocessors from Stanford University
Stanford Webinar with Dan Boneh – Hacking AI: Security & Privacy of Machine Learning Models from Stanford University
Stanford Seminar – Computer-designed organisms – Josh Bongard from Stanford University
Stanford Seminar – Edge Computing in Autonomous Vehicles (panel discussion) from Stanford University
Stanford Seminar – Nanosecond-level Clock Synchronization in a Data Center from Stanford University
Stanford Lecture: Don Knuth—”Dancing Links” (2018) from Stanford University
Stanford Seminar – What’s Next for Blockchain in China? from Stanford University
Stanford Seminar – Scoring (automatically) news articles to fight misinformation from Stanford University
Stanford Seminar – The Evolution of Public Key Cryptography from Stanford University
Stanford Lecture: Don Knuth—”A Conjecture That Had To Be True” (2017) from Stanford University
Stanford Seminar – NVIDIA GPU Computing: A Journey from PC Gaming to Deep Learning from Stanford University
Stanford Seminar – Unethical Algorithms of Massive Scale from Stanford University
Stanford Seminar – Creating Human-Computer Partnerships from Stanford University
Stanford Webinar – Cybersecurity in Modern Era: Zero Knowledge Proofs Explained from Stanford University
Stanford CS236: Deep Generative Models I 2023 I Lecture 4 – Maximum Likelihood Learning from Stanford University
Stanford CS236: Deep Generative Models I 2023 I Lecture 6 – VAEs from Stanford University
Stanford Seminar – When Design = Planning from Stanford University
Stanford Webinar – AI in the Workplace: Rethinking Skill Development from Stanford University
Information, Calcul, Communication: Introduction à la pensée informatique from École Polytechnique Fédérale de Lausanne
Race, Technology, and Algorithmic Bias | Vision & Justice || Radcliffe Institute from Harvard University
Finding Fairness | Cynthia Dwork || Radcliffe Institute from Harvard University
CS50 2012 / Week 3, continued from Harvard University
MIT 6.S191 (2020): Neural Rendering from Alexander Amini
MIT 6.S191 (2020): Generalizable Autonomy for Robot Manipulation from Alexander Amini
MIT 6.S191 (2020): Deep Learning New Frontiers from Alexander Amini
MIT 6.S191 (2021): Reinforcement Learning from Alexander Amini
MIT 6.S191 (2021): Deep Learning New Frontiers from Alexander Amini
MIT 6.S191: Evidential Deep Learning and Uncertainty from Alexander Amini
MIT 6.S191: Deep CPCFG for Information Extraction from Alexander Amini
GTC 2018: Learning Steering for Parallel Autonomy. Alexander Amini from Alexander Amini
MIT 6.S191 (2019): Deep Reinforcement Learning from Alexander Amini
MIT 6.S191 (2019): Deep Learning Limitations and New Frontiers from Alexander Amini
MIT 6.S191 (2019): Image Domain Transfer (NVIDIA) from Alexander Amini
MIT 6.S191 (2020): Convolutional Neural Networks from Alexander Amini
MIT 6.S191 (2020): Reinforcement Learning from Alexander Amini
Introduction to CS and Programming using Python from Massachusetts Institute of Technology
Information Technology as an Integrating Force in Manufacturing from Massachusetts Institute of Technology
Model-based deep learning: beyond unrolling from Yale University
AI in Molecular Imaging from Yale University
The Era of Artificial Intelligence from New York University (NYU)
TensorFlow 2 시작하기 from Imperial College London
MedAI: Training medical image segmentation models with less labeled data | Sarah Hooper from Stanford University
Multimodal medical research of vision and language | Jean-Benoit Delbrouck from Stanford University
David Robinson: Voices in the Code: A Story About People, Their Values, and the Algorithm They Made from Stanford University
Kathleen Creel: Picking on the Same Person: Does Algorithmic Monoculture Homogenize Outcomes? from Stanford University
Carlos Guestrin: How Can You Trust Machine Learning? from Stanford University
Karen Liu: The New Role of Physics Simulation in AI from Stanford University
Leonidas Guibas: Joint Learning Over Visual and Geometric Data from Stanford University
Kathleen Creel: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision-Making Systems from Stanford University
Stanford Seminar – Highly optimized quantum circuits synthesized via data-flow engines from Stanford University
Stanford Seminar – Optimizing the Internet from Stanford University
Statistical Learning: 10.R.1 Neural Networks in R and the MNIST data from Stanford University
CS25 I Stanford Seminar 2022 – Transformer Circuits, Induction Heads, In-Context Learning from Stanford University
CS25 I Stanford Seminar 2022 – DeepMind’s Perceiver and Perceiver IO: new data family architecture from Stanford University
CS25 I Stanford Seminar 2022 – Mixture of Experts (MoE) paradigm and the Switch Transformer from Stanford University
CS25 I Stanford Seminar 2022 – Decision Transformer: Reinforcement Learning via Sequence Modeling from Stanford University
CS25 I Stanford Seminar 2022 – Transformers in Vision: Tackling problems in Computer Vision from Stanford University
Stanford Seminar – Computing with Physical Systems from Stanford University
Stanford Talk: Inequality in Healthcare, AI & Data Science to Reduce Inequality – Improve Healthcare from Stanford University
Stanford Seminar – How can you trust machine learning? Carlos Guestrin from Stanford University
Stanford Seminar – Dataflow for convergence of AI and HPC – GroqChip! from Stanford University
Stanford Seminar – Recent progress in verifying neural networks, Zico Kolter from Stanford University
Stanford Seminar – Emerging risks and opportunities from large language models, Tatsu Hashimoto from Stanford University
The Race for Technological Supremacy: Emerging Technologies and Global Implications from Stanford University
Stanford Seminar – Universal Intelligent Systems by 2030 – Carl Hewitt and John Perry from Stanford University
Stanford Webinar – The Future of Blockchain and Cryptocurrencies: Dan Boneh from Stanford University
Stanford Webinar – Create a Better User Experience through AI, Michael Bernstein from Stanford University
Improving Natural Language Understanding through Adversarial Testing from Stanford University
Stanford Seminar – The Soul of a New Machine: Rethinking the Computer from Stanford University
Stanford Seminar – fastai: A Layered API for Deep Learning from Stanford University
Stanford Seminar – Current Status of tinyML and the Enormous Opportunities Ahead (panel discussion) from Stanford University
Stanford Seminar – Finding the Great Problems from Stanford University
Stanford Seminar – New Chip Technologies for AI and for Asia: the Edge is Where the Action is! from Stanford University
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 4 – Adversarial Attacks / GANs from Stanford University
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 3 – Full-Cycle Deep Learning Projects from Stanford University
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 2 – Deep Learning Intuition from Stanford University
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 1 – Class Introduction & Logistics, Andrew Ng from Stanford University
Stanford Seminar – Extending the theory of ML for human-centric applications from Stanford University
Stanford Seminar – How is AI Evolving? A Look at the Startup Landscape in the U.S. and Asia from Stanford University
Stanford Seminar – Autonomous Driving: AI’s Biggest Endeavor, James Peng of Pony.ai from Stanford University
Stanford Seminar – Virtual Human Agent for Smart City from Stanford University
Stanford Seminar – The Case for Learned Index Structures from Stanford University
Stanford Seminar- Taraxa.io: A Globalized Blockchain Protocol Startup from Stanford University
Stanford Seminar – Artificial Intelligence: Current and Future Paradigms and Implications from Stanford University
Stanford Seminar – Exploring the implications of machine learning for cognitive disabilities from Stanford University
Stanford Seminar – Information Theory of Deep Learning, Naftali Tishby from Stanford University
Stanford Seminar – Tiny functions for codecs, compilation, and (maybe) soon everything from Stanford University
Stanford Seminar – Entrepreneurial Lessons from Self-Driving Cars from Stanford University
Stanford Seminar – Petascale Deep Learning on a Single Chip from Stanford University
Stanford Seminar – Time Well Spent, Tristan Harris from Stanford University
Stanford Seminar – Computing with High-Dimensional Vectors from Stanford University
Stanford Seminar – Multiscale Dataflow Computing: Competitive Advantage at the Exascale Frontier from Stanford University
Stanford Seminar: Autonomous Driving, are we there yet? – Technology, Business, Legal Considerations from Stanford University
Stanford Seminar: Beyond Floating Point: Next Generation Computer Arithmetic from Stanford University
Stanford Seminar: Deep Learning in the Age of Zen, Vega, and Beyond from Stanford University
Stanford Lecture – Don Knuth: The Analysis of Algorithms (2015, recreating 1969) from Stanford University
Stanford Seminar: Designing the iPhone’s Magic Flute – Ge Wang from Stanford University
Stanford Seminar: Challenges in Secure Messaging from Stanford University
Stanford Seminar: The Time-Less Datacenter from Stanford University
Stanford Seminar – Runway: A New Tool for Distributed Systems Design from Stanford University
Bradford Parkinson “GPS for Humanity” – Stanford Engineering Hero Lecture from Stanford University
Stanford Seminar – Leveraging Social Theories to Enhance Human-AI Interaction from Stanford University
Stanford CS109 I Fairness I 2022 I Lecture 26 from Stanford University
Stanford Lecture: Don Knuth – Twintrees, Baxter Permutations, and Floorplans (2022) from Stanford University
Stanford CS25: V3 I Beyond LLMs: Agents, Emergent Abilities, Intermediate-Guided Reasoning, BabyLM from Stanford University
Stanford Webinar – Democratizing Model Discovery with Neural Networks from Stanford University
Stanford CS25: V3 I How I Learned to Stop Worrying and Love the Transformer from Stanford University
Stanford Lecture – Dancing Cells, Dr. Don Knuth I 2023 from Stanford University
Stanford Seminar – The State of Design Knowledge in Human-AI Interaction from Stanford University
Stanford Seminar – Towards Safe and Efficient Learning in the Physical World from Stanford University
Stanford EE274: Data Compression I 2023 I Lecture 18 – Video Compression from Stanford University
Stanford EE274: Data Compression I 2023 I Lecture 1 – Course Intro, Lossless Data Compression Basics from Stanford University
Stanford EE274: Data Compression I 2023 I Lecture 2 – Prefix Free Codes from Stanford University
Stanford EE274: Data Compression I 2023 I Lecture 5 – Asymptotic Equipartition Property from Stanford University
Stanford EE274: Data Compression I 2023 I Lecture 9 – Context-based AC & LLM Compression from Stanford University
Stanford EE274: Data Compression I 2023 I Lecture 4 – Huffman Codes from Stanford University
Stanford EE274: Data Compression I 2023 I Lecture 3 – Kraft Inequality, Entropy, Introduction to SCL from Stanford University
Stanford EE274: Data Compression I 2023 I Lecture 8 – Beyond IID distributions: Conditional entropy from Stanford University
Stanford EE274: Data Compression I 2023 I Lecture 10 – LZ and Universal Compression from Stanford University
Stanford EE274: Data Compression I 2023 I Lecture 6 – Arithmetic Coding from Stanford University
Stanford EE274: Data Compression I 2023 I Lecture 7 – ANS from Stanford University
Stanford CS25: V4 I Overview of Transformers from Stanford University
Stanford CS236: Deep Generative Models I 2023 I Lecture 3 – Autoregressive Models from Stanford University
Stanford CS236: Deep Generative Models I 2023 I Lecture 17 – Discrete Latent Variable Models from Stanford University
Stanford CS236: Deep Generative Models I 2023 I Lecture 13 – Score Based Models from Stanford University
Stanford CS236: Deep Generative Models I 2023 I Lecture 5 – VAEs from Stanford University
Stanford CS236: Deep Generative Models I 2023 I Lecture 16 – Score Based Diffusion Models from Stanford University
Stanford CS236: Deep Generative Models I 2023 I Lecture 18 – Diffusion Models for Discrete Data from Stanford University
Stanford CS236: Deep Generative Models I 2023 I Lecture 2 – Background from Stanford University
Stanford CS236: Deep Generative Models I 2023 I Lecture 12 – Energy Based Models from Stanford University
Stanford CS236: Deep Generative Models I 2023 I Lecture 9 – Normalizing Flows from Stanford University
Stanford CS236: Deep Generative Models I 2023 I Lecture 14 – Energy Based Models from Stanford University
Stanford CS236: Deep Generative Models I 2023 I Lecture 15 – Evaluation of Generative Models from Stanford University
Stanford CS236: Deep Generative Models I 2023 I Lecture 11 – Energy Based Models from Stanford University
Stanford CS236: Deep Generative Models I 2023 I Lecture 7 – Normalizing Flows from Stanford University
Stanford CS236: Deep Generative Models I 2023 I Lecture 1 – Introduction from Stanford University
Stanford CS25: V4 I Jason Wei & Hyung Won Chung of OpenAI from Stanford University
Stanford Seminar – The Human Factors of Formal Methods from Stanford University
Stanford CS25: V4 I Aligning Open Language Models from Stanford University
Stanford CS25: V4 I Demystifying Mixtral of Experts from Stanford University
Stanford CS25: V4 I Transformers that Transform Well Enough to Support Near-Shallow Architectures from Stanford University
Stanford CS25: V4 I From Large Language Models to Large Multimodal Models from Stanford University
Stanford CS25: V4 I Hyung Won Chung of OpenAI from Stanford University
Stanford Seminar – MS&E447: ZKP Panel with Dan Boneh, Jens Groth , Daniel Marin, and Ravi Mhatre from Stanford University
MedAI #124: SleepFM: Multi-modal Representation Learning for Sleep | Rahul Thapa from Stanford University
MedAI #121: HECTOR – Multimodal DL model for recurrence risk in endometrial cancer | Sarah Volinsky from Stanford University
MedAI #120: Holistic OR Domain Modeling with Large Vision Language Models | Ege özsoy from Stanford University
MedAI #119: AI-Driven Advancements in Mammogram Analysis | Aisha Urooj from Stanford University
AI’s Global Impact on Democracy and Governance from Stanford University
Panel: AI’s Role in Improving Health Care Services in Our Communities from Stanford University
AI Transforms Health Care | Artificial Intelligence: The Future of Medicine & Health Care Is Here from Stanford University
Opening Remarks | Artificial Intelligence: The Future of Medicine & Health Care Is Here from Stanford University
MedAI #126: Divide & Conquer – Concept-based Models for Efficient Transfer Learning | Shantanu Ghosh from Stanford University
투자 기술의 혁신: AI from University of Michigan
人、テクノロジー、そしてモビリティの未来 from University of Michigan
사람, 기술, 모빌리티의 미래 from University of Michigan
머신 러닝 기초: 사례 연구 접근 방식 from University of Washington
머신 러닝: 회귀 모델 from University of Washington
Computer Science and IT: Faculty of Engineering and Information Technology Deep Dive from University of Melbourne
The New DBfication of ML/AI from University of Melbourne
Tractable novelty exploration over Continuous and Discrete Sequential Decision Problems from University of Melbourne
Pioneering the Science of Information from University of Melbourne
Convolutions in image processing | Week 1 | MIT 18.S191 Fall 2020 | Grant Sanderson from The Julia Programming Language
Theory of Computation from Massachusetts Institute of Technology
6. The Structures of Power from Yale University
Lessons from the regulatory process for medical software for image analysis and AI from Yale University
Fast motion-resolved 4D MRI using convolutional networks without data consistency from Yale University
Deep subspace learning for dynamic MR image reconstruction from Yale University
Theoretical Interpretation of the ACID for Stabilizing Deep Reconstruction Networks from Yale University
Fast uncertainty estimation and diffusion models in PET from Yale University
Peter Carr Brooklyn Quant Experience (BQE) Seminar Series: Bruno Kamdem from New York University (NYU)
Machine Learning for Personalised Healthcare: Opportunities, Challenges and Insights from New York University (NYU)
Generative Models for Image Synthesis from New York University (NYU)
Learning Representations Using Causal Invariance from New York University (NYU)
Machine Learning for Personalization from New York University (NYU)
The Information Knot Tying Sensing & Action; Emergence Theory of Representation Learning from New York University (NYU)
Andrew Wiles talks to Hannah Fry from University of Oxford
Apprivoiser l’apprentissage automatique from Université de Montréal
ChatGPT pour tous from Université de Montréal

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Applying Data Analytics in Accounting from University of Illinois at Urbana-Champaign
Data Analytics for Business from Georgia Institute of Technology
Health Data Science Foundation from University of Illinois at Urbana-Champaign
Data Use for Disease Control & Global Health Decision-Making from Johns Hopkins University
Big Data Analysis with Scala and Spark (Scala 2 version) from École Polytechnique Fédérale de Lausanne
Dealing With Missing Data from University of Maryland, College Park
Foundations of Data Analytics from The Hong Kong University of Science and Technology
Code Free Data Science from University of California, San Diego
Wearable Technologies and Sports Analytics from University of Michigan
Graph Algorithms in Genome Sequencing from University of California, San Diego
Dynamic Programming: Applications In Machine Learning and Genomics from University of California, San Diego
Data Creation and Collection for Artificial Intelligence via Crowdsourcing from Delft University of Technology
Combining and Analyzing Complex Data from University of Maryland, College Park
Exploratory Data Analysis from Johns Hopkins University
Data Science in Stratified Healthcare and Precision Medicine from University of Edinburgh
Algorithms and Data Structures Capstone from University of California, San Diego
Collaborative Data Science for Healthcare from Massachusetts Institute of Technology
Prediction Models with Sports Data from University of Michigan
Sampling People, Networks and Records from University of Michigan
Data – What It Is, What We Can Do With It from Johns Hopkins University
Computational Reasoning with Microsoft Excel from National University of Singapore
Digital Transformation with Data Analytics Projects from University of Maryland, College Park
Hacking COVID-19 — Course 2: Decoding SARS-CoV-2’s Secrets from University of California, San Diego
Data Mining and Knowledge Discovery from The Hong Kong University of Science and Technology
Design Computing: 3D Modeling in Rhinoceros with Python/Rhinoscript from University of Michigan
Plant Bioinformatics Capstone from University of Toronto
Arranging and Visualizing Data in R from University of Michigan
Big Data Computing with Spark from The Hong Kong University of Science and Technology
Data Analysis: Building Your Own Business Dashboard from Delft University of Technology
Big Data Analytics Using Spark from University of California, San Diego
Making Evidence-Based Strategic Decisions from University of Maryland, College Park
Big Data for Reliability and Security from Purdue University
Big Data – Capstone Project from University of California, San Diego
Advanced Big Data Systems | 高级大数据系统 from Tsinghua University
Politics and Ethics of Data Analytics in the Public Sector from University of Michigan
Design Strategies for Maximizing Total Data Quality from University of Michigan
Mathematical Methods for Data Analysis from The Hong Kong University of Science and Technology
Moneyball and Beyond from University of Michigan
Doing Economics: Measuring Climate Change from University of London International Programmes
Fondamentaux de la science des données from Université de Montréal
Measuring Total Data Quality from University of Michigan
The Total Data Quality Framework from University of Michigan
Big Data Technology Capstone Project from The Hong Kong University of Science and Technology
Big Data Technology Capstone Project from The Hong Kong University of Science and Technology
Big Data Technology Capstone Project from The Hong Kong University of Science and Technology
Big Data Technology Capstone Project from The Hong Kong University of Science and Technology
Mastering Software Development in R Capstone from Johns Hopkins University
Data Visualization Capstone from Johns Hopkins University
Measurement – Turning Concepts into Data from Johns Hopkins University
Quantifying Relationships with Regression Models from Johns Hopkins University
Science des données et santé from Université de Montréal
Fundamentals of Data Analytics in the Public Sector with R from University of Michigan
Data Literacy Capstone – Evaluating Research from Johns Hopkins University
STAT115 2018 from Harvard University
STAT115 2019 from Harvard University
Data Science: A New Way of Thinking | 数据科学导论 from Tsinghua University
مجموعة أدوات عالم البيانات from Johns Hopkins University
How to Describe Data from University of Michigan
Gestion de l’analyse des données from Johns Hopkins University
الحصول على البيانات وتنظيفها from Johns Hopkins University
生物信息学: 导论与方法 from Peking University
MIT Deep Learning in Life Sciences 6.874 Spring 2020 from Massachusetts Institute of Technology
MIT CompBio Course Projects Fall 2019 from Massachusetts Institute of Technology
تحليل البيانات الاستكشافية from Johns Hopkins University
Stanford Seminar – Theories of inference for visual analysis from Stanford University
مقدمة عن البيانات الضخمة from University of California, San Diego
医学统计学与SPSS软件（基础篇） from Peking University
Mining Online Data Across Social Networks from Stanford University
Exploration et production de données pour les entreprises from University of Illinois at Urbana-Champaign
人群与网络 from Peking University
Cours intensif sur la science des données from Johns Hopkins University
Análisis de datos: Diseño y Visualización de Tableros from Delft University of Technology
Stanford Seminar – Towards theories of single-trial high dimensional neural data analysis from Stanford University
Stanford Seminar – Big Data is (at least) Four Different Problems from Stanford University
Stanford Seminar – TSAR (the TimeSeries AggregatoR) Anirudh Todi of Twitter from Stanford University
Big Data Analytics from California Institute of Technology
Data Mining: The Tool of The Information Age from Stanford University
Stanford Webinar – How to Analyze Research Data: Kristin Sainani from Stanford University
Stanford Seminar – Developing Design Spaces for Visualization – Tamara Munzner from Stanford University
Gestión del análisis de datos from Johns Hopkins University
Soccermatics: could a Premier League team one day be managed by a mathematician? from University of Oxford
Introduction to R and Geographic Information Systems (GIS) from Massachusetts Institute of Technology
Mathematics of Big Data and Machine Learning from Massachusetts Institute of Technology
Análisis de datos: Llévalo al MAX() from Delft University of Technology
Управление анализом данных from Johns Hopkins University
Bioinformatics Methods for Transcriptomics from Johns Hopkins University
Muestreo de personas, redes y registros from University of Michigan
Ein Crashkurs in Datenwissenschaft from Johns Hopkins University
Datenanalyse verwalten from Johns Hopkins University
Cómo combinar y analizar datos complejos from University of Maryland, College Park
Stanford Seminar – Forecasting and Predicting the Future of the Future from Stanford University
Stanford Seminar – Jupyter Notebooks and Academic Publication from Stanford University
Stanford Seminar – The End of Privacy from Stanford University
Der Werkzeugkasten des Data Scientist from Johns Hopkins University
Cómo manejar datos faltantes from University of Maryland, College Park
Data Analysis for Social Scientists from Massachusetts Institute of Technology
Method of Detection: The Critical Missing Link in U.S. Cancer Registries from Yale University
Big Data’s Big Deal – Viktor Mayer-Schonberger from University of Oxford
Stanford Seminar – Algorithmic Extremism: Examining YouTube’s Rabbit Hole of Radicalization from Stanford University
Stanford Seminar – Mobilizing the Future from Stanford University
Stanford Seminar: Data For The People, Andreas Weigend of Social Data Lab from Stanford University
EngX: Big Data, Big Impact mini-conference, Russ Altman, Jure Leskovec and Christopher Ré from Stanford University
Stanford Seminar – Zhang Lin on MobileUrban Sensing in Beijing from Stanford University
Stanford Seminar – Human-Machine Symbiosis in Data Visualization from Stanford University
Stanford Seminar – Harnessing Data for Social Impact from Stanford University
파이썬의 데이터 과학 소개 from University of Michigan
파이썬의 응용 소셜 네트워크 분석 from University of Michigan
데이터 과학자의 도구 상자 from Johns Hopkins University
Next in Data Visualization | Arvind Satyanarayan || Radcliffe Institute from Harvard University
Communicating Complex Statistical Ideas to the Public: Lessons from the Pandemic – D. Spiegelhalter from University of Oxford

Information Security (InfoSec) (62)

CS 253 Web Security from Stanford University ★★★★★(40)
Computer Systems Security from Massachusetts Institute of Technology ★★★★★(36)
Web Security Fundamentals from KU Leuven University ★★★★☆(21)
Stanford Seminar – Engineering Cyber Resiliency: A Pragmatic Approach from Stanford University ★★★★★(17)
Stanford Webinar – Hash, Hack, Code: Emerging Trends in Cyber Security from Stanford University ★★★★★(9)
Intro to Information Security from Georgia Institute of Technology ★★☆☆☆(2)
Network Security from Georgia Institute of Technology ★★★★★(1)
Introduction to Cyber Attacks from New York University (NYU) ★★★★★(1)
CS50’s Introduction to Cybersecurity from Harvard University ★★★★☆(1)
Cybersecurity for Everyone from University of Maryland, College Park ★★★★★(1)
Cyber Security Economics from Delft University of Technology ★★☆☆☆(1)
Real-Time Cyber Threat Detection and Mitigation from New York University (NYU)
Introduction to Cybersecurity from University of Washington
Cyber Attack Countermeasures from New York University (NYU)
Cyber-Physical Systems Security from Georgia Institute of Technology
Building a Cybersecurity Toolkit from University of Washington
Cybersecurity: The CISO’s View from University of Washington
Finding Your Cybersecurity Career Path from University of Washington
Enterprise and Infrastructure Security from New York University (NYU)
Introduction to DevSecOps from Johns Hopkins University
A Strategic Approach to Cybersecurity from University of Maryland, College Park
Cybersecurity Capstone Project from University of Maryland, College Park
Leadership from Infosec
Mobile Payment Security from New York University (NYU)
La cybersécurité en milieu universitaire from Université de Montréal
Stanford Webinar – Securing the World Around Us: Cyber Security for the Physical Economy from Stanford University
The Growing Threat and Impact of Web-Based Malware – Stanford Computer Security from Stanford University
Stanford Seminar – The Current State of Cybersecurity from Stanford University
Stanford Seminar – Solving Cybersecurity as an Economic Problem from Stanford University
Security Challenges in 5G Wireless and Beyond from New York University (NYU)
Webinar – Big Breaches: What We Learned From the World’s Most Disruptive Cybersecurity Attacks from Stanford University
Taking Memory Forensics to the Next Level from New York University (NYU)
Stanford Seminar – Preventing Successful Cyberattacks Using Strongly-typed Actors from Stanford University
Stanford Webinar: Building Your Shield: Mapping the Cybersecurity Market, Dan Boneh and Neil Daswani from Stanford University
Stanford Seminar – Tales from the Risks Forum from Stanford University
Stanford Webinar: To Attribute or Not Attribute, Is That the Question? from Stanford University
Stanford Seminar – Locking the Web Open–a Call for a New, Decentralized Web from Stanford University
Stanford Seminar – Online Political Ad Transparency from Stanford University
Stanford Seminar – Computer Security: The Mess We’re In, How We Got Here, and What to Do About It from Stanford University
Stanford Seminar – Thunderclap & CHERI (Capability Hardware-Enhanced RISC Instructions) from Stanford University
Bulletproofs: Short Proofs for Confidential Transactions and More from Stanford University
Stanford Seminar – Lessons from Mirai and the Current State of IoT Security from Stanford University
Stanford Seminar – Exploiting modern microarchitectures: Meltdown, Spectre, & other hardware attacks from Stanford University
Stanford Seminar – RowHammer, RowPress and Beyond: Can We Be Free of Bitflips (Soon)? from Stanford University
Stanford Seminar – How Can Privacy Exist in a Data-Driven World? from Stanford University
디지털 민주주의의 명암 from University of Michigan
Proving confidentiality and its preservation for mixed-sensitivity concurrent programs from University of Melbourne
Industry Insights: Cyber Security from University of Melbourne
A conversation with a recent Tandon Cyber Fellow grad, Michael Leking (Tandon ’21) from New York University (NYU)
Anatomy of an Attack: What Really Happens and How To Protect Your Enterprise from New York University (NYU)
Trading Privacy for Convenience in the Age of Technology: Part 2 from New York University (NYU)
CSAW’21 Keynote | Cybersecurity: Keeping the Lights On, Dr. Martin Otto, Siemens Technology from New York University (NYU)
IC Layout Security from New York University (NYU)
Conversations at the Forefront of Cybersecurity with NYU CCS featuring Nasir Memon from New York University (NYU)
Conversations at the Forefront of Cyber Security with NYU CCS featuring Joel Caminer from New York University (NYU)
Conversations at the Forefront of Cybersecurity with NYU CCS featuring Randy Milch from New York University (NYU)
Conversations at the Forefront of Cyber Security with NYU CCS featuring Dr. Justin Cappos from New York University (NYU)
Conversations at the Forefront of Cyber Security with NYU CCS featuring Dr. Damon McCoy from New York University (NYU)
Dispelling the Top 10 Myths in Cybersecurity from New York University (NYU)
9th Cyber Security Lecture Sponsored by AIG from New York University (NYU)
8. Ryan Stortz & Kareem El-Faramawi – Firing Rounds at the Analysis Shooting Gallery from New York University (NYU)
2. Andrew Dutcher – angr from New York University (NYU)

Manoel Cortes Mendez

Software engineer and online graduate student in computer science passionate about education, technology, and their intersection.

Suparn Patra

Suparn developed an interest in MOOCs through a project he worked on during his MSc in Artificial Intelligence. He joined Class Central as a Full Stack Engineer.

Comments 4

Ambreen Asif Qureshi 4/11/2020 at 1:09am

I am an educationist since 1994 and am presently working as Director Quality Enhancement Cell at one of the private universities in Pakistan.

I am pretty good at almost all MS Office applications, and an avid user of computers for daily professional work.

However, I think post Covid-19, there will be lots of lay offs through the the world, and, God forbid, I may not be an exception considering my salary package and age.

Therefore, I want to gain knowledge and brush up my qualifications which may help me in working online from Home. Can you please advise me what would match my experience (mainly University Administration and Quality Assurance in Higher Education) and qualifications (M.A. English).

I want to remain gainfully employed. Please help me understand new technologies and equip myself for future.

XYG 4/29/2020 at 5:47pm

Very helpful list. Thanks very much!

Miria Cambie 3/3/2023 at 11:17am

This is a wonderful resource. Thank you!

- Nasheema Fathali Shahbazi 9/28/2023 at 12:51am
  
  Ambreen, I would say input your above question into ChatGTP and the answer following will guide you as to what to study that will align with what you looking for.
  
  Also I want to say thank you so much for making this resource available for so many people. This is an excellent compilation. Hats off to you .

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Comments 4

Ambreen Asif Qureshi 4/11/2020 at 1:09am

XYG 4/29/2020 at 5:47pm

Miria Cambie 3/3/2023 at 11:17am

Nasheema Fathali Shahbazi 9/28/2023 at 12:51am

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