Online Master of Science in Computer Science (OMSCS)

Learn how to program all the major systems of a robotic car from the leader of Google and Stanford's autonomous driving teams. This class will teach you basic methods in Artificial Intelligence, including: probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics. Extensive programming examples and assignments will apply these methods in the context of building self-driving cars.

This course is offered as part of the Georgia Tech Masters in Computer Science. The updated course includes a final project, where you must chase a runaway robot that is trying to escape!

In this course, we will see all the advances that have led to the state-of-the-art operating system that we know today, covering variety of platforms -- cell phones, multi-core, parallel systems, distributed systems, and cloud computing.

This class is offered as CS6290 at Georgia Tech where it is a part of the Online Masters Degree (OMS). Taking this course here will not earn credit towards the OMS degree.

The course begins with a lesson on performance measurement, which leads to a discussion on the necessity of performance improvement.

Pipelining, the first level of performance refinement, is reviewed. The weaknesses of pipelining will be exposed and explored, and various solutions to these issues will be studied. The student will learn hardware, software, and compiler based solutions to these issues.

This class is offered as CS7641 at Georgia Tech where it is a part of the Online Masters Degree (OMS). Taking this course here will not earn credit towards the OMS degree.

Machine Learning is a graduate-level course covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences.

The first part of the course covers Supervised Learning, a machine learning task that makes it possible for your phone to recognize your voice, your email to filter spam, and for computers to learn a bunch of other cool stuff.

In part two, you will learn about Unsupervised Learning. Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? Such answers can be found in this section!

Finally, can we program machines to learn like humans? This Reinforcement Learning section will teach you the algorithms for designing self-learning agents like us!

This course provides an introduction to computer vision including fundamentals of image formation, camera imaging geometry, feature detection and matching, multiview geometry including stereo, motion estimation and tracking, and classification. We’ll develop basic methods for applications that include finding known models in images, depth recovery from stereo, camera calibration, image stabilization, automated alignment (e.g. panoramas), tracking, and action recognition. We focus less on the machine learning aspect of CV as that is really classification theory best learned in an ML course.

The focus of the course is to develop the intuitions and mathematics of the methods in lecture, and then to learn about the difference between theory and practice in the problem sets. All algorithms work perfectly in the slides. But remember what Yogi Berra said: In theory there is no difference between theory and practice. In practice there is. (Einstein said something similar but who knows more about real life?) In this course you do not, for the most part, apply high-level library functions but use low to mid level algorithms to analyze images and extract structural information.

This class explores how computation impacts the entire workflow of photography, which is traditionally aimed at capturing light from a 3D scene to form a 2D image. A detailed study of the perceptual, technical and computational aspects of forming pictures, and more precisely the capture and depiction of reality on a (mostly 2D) medium of images is undertaken over the entire term. The scientific, perceptual, and artistic principles behind image-making will be emphasized, especially as impacted and changed by computation.

Topics include the relationship between pictorial techniques and the human visual system; intrinsic limitations of 2D representations and their possible compensations; and technical issues involving capturing light to form images. Technical aspects of image capture and rendering, and exploration of how such a medium can be used to its maximum potential, will be examined. New forms of cameras and imaging paradigms will be introduced.

This class is offered as CS6505 at Georgia Tech where it is a part of the Online Masters Degree (OMS). Taking this course here will not earn credit towards the OMS degree.

In this course, we will ask the big questions, "What is a computer? What are the limits of computation? Are there problems that no computer will ever solve? Are there problems that can’t be solved quickly? What kinds of problems can we solve efficiently and how do we go about developing these algorithms?" Understanding the power and limitations of algorithms helps us develop the tools to make real-world computers smarter, faster and safer.

This class is offered as CS7637 at Georgia Tech where it is a part of the Online Masters Degree (OMS). Taking this course here will not earn credit towards the OMS degree.

This is a core course in artificial intelligence. It is designed to be a challenging course, involving significant independent work, readings, assignments, and projects. It covers structured knowledge representations, as well as knowledge-based methods of problem solving, planning, decision-making, and learning.

The class is organized around three primary learning goals. First, this class teaches the concepts, methods, and prominent issues in knowledge-based artificial intelligence. Second, it teaches the specific skills and abilities needed to apply those concepts to the design of knowledge-based AI agents. Third, it teaches the relationship between knowledge-based artificial intelligence and the study of human cognition.

This course introduces students to the real world challenges of implementing machine learning based trading strategies including the algorithmic steps from information gathering to market orders. The focus is on how to apply probabilistic machine learning approaches to trading decisions. We consider statistical approaches like linear regression, KNN and regression trees and how to apply them to actual stock trading situations.

Data science plays an important role in many industries. In facing massive amount of heterogeneous data, scalable machine learning and data mining algorithms and systems become extremely important for data scientists. The growth of volume, complexity and speed in data drives the need for scalable data analytic algorithms and systems. In this course, we study such algorithms and systems in the context of healthcare applications.

In healthcare, large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). This data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment.

The goal of this course is to give you solid foundations for developing, analyzing, and implementing parallel and locality-efficient algorithms. This course focuses on theoretical underpinnings. To give a practical feeling for how algorithms map to and behave on real systems, we will supplement algorithmic theory with hands-on exercises on modern HPC systems, such as Cilk Plus or OpenMP on shared memory nodes, CUDA for graphics co-processors (GPUs), and MPI and PGAS models for distributed memory systems.

This course is a graduate-level introduction to scalable parallel algorithms. "Scale" really refers to two things: efficient as the problem size grows, and efficient as the system size (measured in numbers of cores or compute nodes) grows. To really scale your algorithm in both of these senses, you need to be smart about reducing asymptotic complexity the way you’ve done for sequential algorithms since CS 101; but you also need to think about reducing communication and data movement. This course is about the basic algorithmic techniques you’ll need to do so.

The techniques you’ll encounter covers the main algorithm design and analysis ideas for three major classes of machines: for multicore and many core shared memory machines, via the work-span model; for distributed memory machines like clusters and supercomputers, via network models; and for sequential or parallel machines with deep memory hierarchies (e.g., caches). You will see these techniques applied to fundamental problems, like sorting, search on trees and graphs, and linear algebra, among others. The practical aspect of this course is implementing the algorithms and techniques you’ll learn to run on real parallel and distributed systems, so you can check whether what appears to work well in theory also translates into practice. (Programming models you’ll use include Cilk Plus, OpenMP, and MPI, and possibly others.)

This is the second course in the 3-course Machine Learning Series and is offered at Georgia Tech as CS7641. Taking this class here does not earn Georgia Tech credit.

Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? The answer can be found in Unsupervised Learning!

Closely related to pattern recognition, Unsupervised Learning is about analyzing data and looking for patterns. It is an extremely powerful tool for identifying structure in data. This course focuses on how you can use Unsupervised Learning approaches -- including randomized optimization, clustering, and feature selection and transformation -- to find structure in unlabeled data.

Series Information: Machine Learning is a graduate-level series of 3 courses, covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences.

The entire series is taught as an engaging dialogue between two eminent Machine Learning professors and friends: Professor Charles Isbell (Georgia Tech) and Professor Michael Littman (Brown University).

This class is offered as CS6300 at Georgia Tech where it is a part of the Online Masters Degree (OMS). Taking this course here will not earn credit towards the OMS degree.

In SDP, you will learn how to select and implement the ideal software process for your development project. Through Professor Orso's engaging examples and interviews with industry insiders, you will learn both conceptual and practical aspects of software engineering. The course covers requirements engineering, architecture and design, testing and maintenance, and software quality in general.

The goal of this class is to equip you with the skills necessary to define requirements, set up an integrated development environment (IDE), learn Git (and Github!) and use Unified Modeling Language (UML) to design and build an Android application. We will also examine several testing practices and refactoring techniques that are helpful before the launch of your software project.

While everyone working with software should have these skills, they are particularly important for Software Engineers and Engineering Managers.

This class is offered as CS6250 at Georgia Tech where it is a part of the Online Masters Degree (OMS). Taking this course here will not earn credit towards the OMS degree.

This course covers advanced topics in Computer Networking such as Software-Defined Networking (SDN), Data Center Networking and Content Distribution. The course is divided into three parts:

Part 1 is about the implementation, design principles and goals of a Computer Network and touches upon the various routing algorithms used in CN (such as link-state and distance vector).

Part 2 talks about resource control and content distribution in Networking Applications. It covers Congestion Control and Traffic Shaping.

Part 3 deals with the operations and management of computer networks encompassing SDN's (Software Defined Networks), Traffic Engineering and Network Security.

Software Architecture and Design teaches the principles and concepts involved in the analysis and design of large software systems. This course is split into four sections: (1) Introduction, (2) UML and Analysis, (3) Software Architecture, and (4) Software Design.

Introduction to Operating Systems is a graduate-level introductory course in operating systems. This course teaches the basic operating system abstractions, mechanisms, and their implementations. The core of the course contains concurrent programming (threads and synchronization), inter process communication, and an introduction to distributed operating systems. The course is split into four sections: (1) Introduction, (2) Process and Thread Management, (3) Resource Management and Communication, and (4) Distributed Systems.

This course provides a one-semester overview of information security. It is designed to help students with
prior computer and programming knowledge — both undergraduate and graduate — understand this important
priority in society today. The technical content of the course gives a broad overview of essential concepts and
methods for providing and evaluating security in information processing systems (operating systems and
applications, networks, protocols, and so on).

In addition to its technical content, the course touches on the
importance of management and administration, the place information security holds in overall business risk,
social issues such as individual privacy, and the role of public policy.

This class is offered as CS6460 at Georgia Tech where it is a part of the Online Masters Degree (OMS). Credit for taking this course is conferred only on those that enroll in the degree. The text in the following sections comes from the syllabus for CS6460, located here.

This class is simultaneously an introductory course about educational technology and an advanced, project-oriented class on designing or researching technology's intersection with education. As such, the course provides information about a large number of topics within educational technology, including pedagogical strategies, research methodologies, current tools, open problems, and broader issues. The scope of the material provided goes beyond what any one person could reasonably learn in a semester. Instead, you will select those areas that appeal to you or that support your ultimate project ideas. For example, if you're interested in research, you may focus on the applicable research methodologies to your chosen area of investigation, relevant pedagogical strategies or theories, and the current state-of-the-art within that community. If you're interested in design, you may focus on the relevant pedagogical strategies or theories for your chosen domain, the current popular tools within that domain, and open problems that need to be addressed.

Learn about the fundamentals of Artificial Intelligence in this introductory graduate-level course. It provides a survey of various topics in the field along with in-depth discussion of foundational concepts such as classical search, probability, machine learning, logic and planning.

Data and visual analytics is an emerging field concerned with analyzing, modeling, and visualizing complex high dimensional data.

This course will introduce students to the field by covering state­-of-­the-art modeling, analysis and visualization techniques. It will emphasize practical challenges involving complex real world data and include several case studies and hands-on work with the R programming language.

This course is an introductory course on human-computer interaction, covering the principles, techniques, and open areas of development in HCI.

This course covers the theory and practice of software analysis, which lies at the heart of many software development processes such as diagnosing bugs, testing, debugging, and more. It presents diverse techniques, each with their own strengths and limitations, for automating tasks such as testing, debugging, and finding bugs in complex real-world programs. These techniques include dataflow analysis, constraint-based analysis, type systems, model checking, symbolic execution, and more. The course teaches the principles underlying these techniques as well as imparts hands-on experience with using and implementing tools based on these techniques.

This course provides an introduction to security issues relating to various cyber-physical systems including industrial control systems and those considered critical infrastructure systems.

The summary for this course comes from the course web site at Georgia Tech. Note that watching this course on Udacity does not count for Georgia Tech credit unless you are enrolled in the Georgia Tech OMSCS program.

This course provides an introduction to computer and network security. Students successfully completing this class will be able to evaluate works in academic and commercial security, and will have rudimentary skills in security research. The course begins with a tutorial of the basic elements of cryptography, cryptanalysis, and systems security, and continues by covering a number of seminal papers and monographs in a wide range of security areas.

Topics covered include network security, authentication, security protocol design and analysis, security modeling, trusted computing, key management, program safety, intrusion detection, DDOS detection and mitigation, architecture/operating systems security, security policy, group systems, biometrics, web security, and other emerging topics.

Most of the course readings will come from seminal papers in the field. Links to these papers will be provided on the course pages. In addition, links to critical reference materials will also be provided.

In the 21st century, embedded systems are the systems of future with cellular phones, smart-phones, tablets becoming the dominant platforms for computing and communication. The ubiquity of information and the associated need for the computation that accompanies it is driving this revolution only to be accelerated by the new paradigms such as the Internet-of-Things (IoT). These platforms are clearly very different in terms of their processing requirements which are very unique: real-time needs, high performance but at low energy, compact-code and data segments, and most importantly ever changing software stack. Such unique requirements have led to a complete redesign and reinvention of the both hardware and the software stack from ground up, for example, brand new processors such as ARM, DSPs, network processors were invented all the way up to new virtual machines such as Dalvik, new operating systems such as Android and new programming models and compiler optimizations.

The objective of this course is to learn the theory and practice behind building automatic translators (compilers) for higher level programming languages and to engineer and build key phases of a compiler in Java or C++ for a small language.

This course presents an example of applying a database application development methodology to a major real-world project.