Kernel Methods Courses and Certifications

Alan Turing Institute

The Mother of All Representer Theorems for Inverse Problems and Machine Learning - Michael Unser

Explore the mathematical foundations of inverse problems and machine learning, focusing on representer theorems, Banach spaces, and kernel methods for advanced data science applications.

• YouTube
• 48 minutes
• On-Demand
• Free Video

MITCBMM

Fit Without Fear - An Over-Fitting Perspective on Modern Deep and Shallow Learning

Explore over-fitting in modern machine learning, challenging conventional wisdom and presenting kernel methods as a competitive alternative to deep learning. Insights on interpolation, generalization, and stochastic gradient descent.

• YouTube
• 55 minutes
• On-Demand
• Free Video

HUJI Machine Learning Club

Gauss-Legendre Features for Scalable Gaussian Process Regression

Discover how Gauss-Legendre features enable efficient scaling of Gaussian process regression through low-rank kernel matrix approximation, offering faster computation for large datasets with poly-logarithmic feature requirements.

• YouTube
• 1 hour 5 minutes
• On-Demand
• Free Video

Stanford CS229 - Machine Learning - Summer 2019 - Kernel Methods & Support Vector Machine

Explore kernel methods and Support Vector Machines in machine learning, focusing on advanced techniques for data classification and pattern recognition in complex datasets.

• YouTube
• 1 hour 55 minutes
• On-Demand
• Free Video

Deep Learning Essentials

Explore neural networks, perceptrons, and deep learning fundamentals. Master backpropagation, stochastic gradient descent, and kernel methods through Python coding assignments.

• Coursera
• 15 hours 41 minutes
• On-Demand
• Free Online Course (Audit)

Cracking Classification

Master essential classification algorithms including logistic regression, decision trees, KNN, Naive Bayes, and SVM. Learn to evaluate model performance and apply techniques to real-world datasets.

• CodeSignal
• On-Demand
• Free Certificate

Statistical Learning

Explore cutting-edge statistical analysis techniques, from regression to advanced methods like kernel smoothing and support vector machines. Enhance data interpretation skills with hands-on Python examples.

• Coursera
• 4 days 19 hours 15 minutes
• On-Demand
• Free Online Course (Audit)

University of Tübingen

Statistical Machine Learning

2 reviews

Comprehensive exploration of machine learning concepts, from basic algorithms to advanced topics like kernels, dimensionality reduction, and clustering, with insights on societal impact and practical applications.

• YouTube
• 1 day 9 hours
• On-Demand
• Free Online Course

MITCBMM

Interpolation and Learning With Scale Dependent Kernels

Explore scale-dependent kernels in machine learning, covering overfitting, statistical theory, and practical applications in regression and interpolation.

• YouTube
• 1 hour 4 minutes
• On-Demand
• Free Video

Institute for Advanced Study

Successes and Challenges in Neural Models for Speech and Language - Michael Collins

Explore neural models' impact on speech and language processing, examining successes, challenges, and key architectures in parsing, word embeddings, and transformers.

• YouTube
• 45 minutes
• On-Demand
• Free Video

Institute for Advanced Study

On the Connection Between Neural Networks and Kernels: A Modern Perspective - Simon Du

Explore connections between neural networks and kernels, focusing on over-parameterization, generalization, and neural tangent kernels. Insights on training dynamics and applications to various learning tasks.

• YouTube
• 31 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

From A to B via a Synthesis of Rare-Event Sampling and Machine Learning

Explore machine learning and rare-event sampling techniques for analyzing molecular transitions and generating pathways between energy basins, with applications in materials science and biomolecules.

• YouTube
• 45 minutes
• On-Demand
• Free Video

Applied Algebraic Topology Network

Extracting Persistence Features with Hierarchical Stabilisation

Explore hierarchical stabilisation for extracting stable invariants from persistence modules, focusing on the stable rank and its applications in data analysis and classification tasks.

• YouTube
• 43 minutes
• On-Demand
• Free Video

Applied Algebraic Topology Network

On the Metric Distortion of Embedding Persistence Diagrams into RKHS

Explores embedding Persistence Diagrams into Reproducing Kernel Hilbert Spaces, analyzing metric properties and stability guarantees for machine learning applications in Topological Data Analysis.

• YouTube
• 55 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Generalization Theory in Machine Learning

Explore generalization theory in machine learning, covering traditional methods, deep learning, and statistical learning theory. Gain insights into overcoming the curse of dimensionality and understanding learning bounds.

• YouTube
• 1 hour 11 minutes
• On-Demand
• Free Video