Kernel Methods Courses and Certifications

Fields Institute

Kernel Methods for Machine Learning and Dynamical Systems

Explore advanced kernel methods in machine learning and dynamical systems through expert talks on Gaussian processes, functional data analysis, and sparse factorization techniques.

• YouTube
• 9 hours 2 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

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

Simons Institute

Linear Representations of Concepts in Modern AI Models

Discover how concepts are linearly encoded in neural networks using probes and Recursive Feature Machines to extract human knowledge from Large Language Models.

• YouTube
• 37 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

Institute for Pure & Applied Mathematics (IPAM)

Some Novel Kernel-Based Divergences Between Probability Distributions

Explore novel kernel-based divergences between probability distributions, focusing on the Kullback-Leibler divergence with kernel covariance operators (KKL) and its regularized variant for distributions with disjoint supports.

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

Harvard CMSA

Implicit Regularization via Uniform Convergence in Linear Models and Kernel Methods

Explore the statistical analysis of mirror descent algorithms and their convergence properties in machine learning, focusing on early-stopped methods and uniform learning theory applications.

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

Conference GSI

Information Theory with Kernel Methods

Explore entropy estimation in data science using kernel methods and quantum information theory for covariance matrices.

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

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
• 3 hours
• On-Demand
• Free Certificate

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
• Paid Course

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 18 hours 15 minutes
• On-Demand
• Paid Course

Statistical Learning for Engineering Part 1

Explore supervised and unsupervised machine learning algorithms, from linear regression to SVMs, with hands-on Python implementation for real-world engineering applications.

• Coursera
• 1 day 11 hours 38 minutes
• On-Demand
• Paid Course

Statistical Learning for Engineering Part 2

Explore advanced machine learning algorithms including neural networks, clustering, dimensionality reduction, and reinforcement learning with hands-on Python implementation.

• Coursera
• 1 day 46 minutes
• On-Demand
• Paid Course

Simons Institute

Kernel Methods for Causal Inference

Explore kernel ridge regression for causal inference, covering dose responses, treatment effects, and extensions to various settings. Learn about consistency, efficiency, and robustness in estimation.

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