Statistical Learning Theory Courses and Certifications

Harvard CMSA

Graduate Topics in Deep Learning Theory

Explore advanced theoretical foundations of deep learning through graduate-level mathematical analysis and computational principles taught by Harvard CMSA.

• YouTube
• 13 hours 15 minutes
• Self-Paced
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Computational vs Statistical Gaps in Learning and Optimization

Explore computational vs statistical gaps in machine learning through expert talks on optimization, privacy, robustness, and algorithmic fairness challenges.

• YouTube
• 17 hours 9 minutes
• Self-Paced
• Free Video

Simons Institute

The Information Bottleneck Theory of Deep Neural Networks

Explore the Information Bottleneck Theory's application to deep neural networks, examining statistical learning, information theory, and stochastic gradient descent in brain data analysis.

• YouTube
• 1 hour 38 minutes
• Self-Paced
• Free Video

NPTEL-NOC IITM

Logistic Regression: Introduction to Binary Classification and Linear Classifiers

Master logistic regression for binary classification, covering linear classifiers, sigmoid functions, parameter estimation, and log-likelihood functions.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

Paul G. Allen School

Statistical Learning I - Foundations and Principles

Explore statistical learning fundamentals with Robert Schapire from Microsoft Research, covering key concepts and applications in machine learning and data analysis.

• YouTube
• 1 hour 5 minutes
• Self-Paced
• Free Video

Yacine Mahdid

Learning Machine Learning Efficiently

1 rating

Discover an efficient approach to learning machine learning, exploring the landscape, optimal paths, and practical strategies for mastering this complex field.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

Steve Brunton

Bayesian Inference - Overview

Discover how Bayesian inference balances data with prior beliefs to learn probability distributions, including practical hypothesis testing and key limitations.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

Paul G. Allen School

Algorithmic Foundations of Learning and Control - 2019

Explore algorithmic foundations connecting machine learning and control theory through expert lectures from leading researchers in optimization, reinforcement learning, and robotics.

• YouTube
• 19 hours 31 minutes
• Self-Paced
• Free Video

Institut Henri Poincaré

Structured Regularization Summer School 2017

Dive into advanced mathematical techniques for structured regularization through expert lectures covering optimization, machine learning, and signal processing applications.

• YouTube
• 22 hours 32 minutes
• Self-Paced
• Free Video

Simons Institute

Distribution Shift as Underspecification, and What We Might Do About It

Explore strategies for addressing distribution shift in deep learning, focusing on underspecification and potential solutions for more robust AI systems.

• YouTube
• 1 hour 1 minute
• Self-Paced
• Free Video

International Centre for Theoretical Sciences

Advances in Applied Probability II

Explore cutting-edge research in applied probability, covering statistical learning, high-dimensional computation, Monte Carlo methods, discrete probability, percolation, and empirical methods. Gain insights from leading experts.

• YouTube
• 3 hours 2 minutes
• Self-Paced
• Free Video

Alan Turing Institute

Learning Automata with Hankel Matrices - Borja Balle, Amazon Research Cambridge

Explore a unified framework for learning automata using Hankel matrices, covering query learning, spectral learning, and matrix completion algorithms with practical applications.

• YouTube
• 41 minutes
• Self-Paced
• Free Video

Paul G. Allen School

Statistical Learning - Robert Schapire, Microsoft Research

Explore statistical learning with Robert Schapire, covering strong/weak learning, boosting, and confidence in machine learning algorithms.

• YouTube
• 1 hour 6 minutes
• Self-Paced
• Free Video

Paul G. Allen School

Statistical Learning IV - Robert Schapire, Microsoft Research

Explore statistical learning with Robert Schapire, covering expected value theorems, ghost samples, PAC learning, and finite hypothesis spaces in this comprehensive lecture.

• YouTube
• 1 hour 1 minute
• Self-Paced
• Free Video

Institute for Advanced Study

Learning Probability Distributions - What Can, What Can't Be Done - Shai Ben-David

Explore probability distribution learning, its possibilities and limitations. Shai Ben-David discusses density estimation, sample compression schemes, and challenges in statistical learning theory.

• YouTube
• 57 minutes
• Self-Paced
• Free Video