Statistical Learning Theory Courses and Certifications

Simons Institute

Boot Camp on Generalization Theory for Graph Learning

Explore generalization theory fundamentals for graph learning through expert insights from RWTH Aachen University and MIT researchers.

• YouTube
• 1 hour 40 minutes
• On-Demand
• Free Video

IEEE

Understanding Deep Learning - Challenges for Statistical Learning Theory

Explore statistical learning theory challenges in deep learning, focusing on key concepts and recent developments in this complex field.

• YouTube
• 1 hour 28 minutes
• On-Demand
• Free Video

IEEE

Learning to Reconstruct - Statistical Learning Theory and Encrypted Database Attacks

Explores the connection between statistical learning theory and encrypted database attacks, presenting novel approaches for database reconstruction from access pattern leakage with improved efficiency and broader applicability.

• YouTube
• 21 minutes
• On-Demand
• Conference Talk

统计学习理论及应用

掌握统计机器学习的核心方法，从回归模型到深度学习，结合数学推导与实际应用案例，系统学习感知机、支持向量机、集成方法等技术。

• XuetangX
• On-Demand
• Free Online Course

Centre International de Rencontres Mathématiques

Regularization via Permutation of Labels - Lecture 1

Explore regularization techniques using label permutation in mathematical statistics, focusing on innovative approaches and their applications.

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

Centre International de Rencontres Mathématiques

Regularization via Permutation of Labels - Lecture 2

Explore advanced statistical techniques for regularization through label permutation, focusing on mathematical theory and practical applications.

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

International Centre for Theoretical Sciences

From Information Theory to Learning via Statistical Physics - Introduction

Explore connections between information theory, statistical physics, and learning. Delve into classical statistics, high-dimensional problems, and Bayesian approaches, with applications in signal processing and regression.

• YouTube
• 1 hour 33 minutes
• On-Demand
• Free Video

Harvard CMSA

Are Gaussian Data All You Need for Machine Learning Theory - A Statistical Physics Perspective

Delve into the theoretical foundations of Gaussian assumptions in machine learning, exploring their surprising effectiveness and limitations in high-dimensional statistics and modeling.

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

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

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
• On-Demand
• 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
• On-Demand
• Free Video

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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• Free Video

IPhT-TV

Mathematical Problems in Machine Learning - Part 1 of 4

Explore mathematical foundations of machine learning with Andrea Montanari, focusing on key problems and theoretical concepts in the field.

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