Theoretical Machine Learning Courses and Certifications

Institute for Advanced Study

On the Critic Function of Implicit Generative Models - Arthur Gretton

Explore implicit generative models' critic function, divergence measures, and energy-based models with Arthur Gretton. Gain insights into topological properties, neural net divergences, and multimodality in machine learning.

• YouTube
• 1 hour 6 minutes
• On-Demand
• Free Video

Institute for Advanced Study

Priors for Semantic Variables - Yoshua Bengio

Explore semantic variables, learning theory, and cognitive processes with Yoshua Bengio. Delve into attention mechanisms, causality, and modular recurrent networks for advanced machine learning insights.

• YouTube
• 1 hour 32 minutes
• On-Demand
• Free Video

Institute for Advanced Study

Instance-Hiding Schemes for Private Distributed Learning

Explore private distributed learning techniques, including instance-hiding schemes, cryptographic methods, and data augmentation, with insights on privacy laws and practical applications.

• YouTube
• 1 hour 14 minutes
• On-Demand
• Free Video

Institute for Advanced Study

Interpolation in Learning - Steps Towards Understanding When Overparameterization Is Harmless, When It Helps, and When It Causes Harm

Explore overparameterization in machine learning, its benefits, potential harm, and the double descent phenomenon. Gain insights into interpolation regimes and matrix intuition.

• YouTube
• 1 hour 23 minutes
• On-Demand
• Free Video

Institute for Advanced Study

Optimization Landscape and Two-Layer Neural Networks - Rong Ge

Explore optimization landscapes and two-layer neural networks, focusing on non-convexity, saddle points, and symmetric distributions in theoretical machine learning.

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

Simons Institute

Toward Understanding In-Context Learning

Dive into the mechanisms of in-context learning in large language models, exploring theoretical explanations and simplified data distributions with Stanford's Tengyu Ma.

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

Simons Institute

A Brief Introduction to Theoretical Foundations of Machine Learning and Machine Teaching

Explore theoretical foundations of machine learning and machine teaching, focusing on synthesis of models and systems in this comprehensive lecture by Jerry Zhu from University of Wisconsin-Madison.

• YouTube
• 2 hours 11 minutes
• On-Demand
• Free Video

Fields Institute

Weaving Together Machine Learning, Theoretical Physics, and Neuroscience

Exploring the intersection of machine learning, physics, and neuroscience through complex systems analysis to develop better algorithms and understand neural systems.

• YouTube
• 1 hour 6 minutes
• On-Demand
• Free Video

Alan Turing Institute

Machine Learning in Theoretical and Experimental High Energy Physics

Explore machine learning's impact on high energy physics, from data analysis to theoretical advancements, and its role in the search for new physics discoveries.

• YouTube
• 1 hour 6 minutes
• On-Demand
• Free Video

Institute for Advanced Study

Latent State Recovery in Reinforcement Learning - John Langford

Explore latent state recovery in reinforcement learning, covering core problems, alternative approaches, and state abstraction. Gain insights from John Langford's expertise in theoretical machine learning.

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

Institute for Advanced Study

Latent Stochastic Differential Equations for Irregularly-Sampled Time Series - David Duvenaud

Explore advanced techniques for analyzing irregularly-sampled time series data using latent stochastic differential equations, with applications in predictive modeling and continuous-time machine learning.

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

Institute for Advanced Study

Your Brain on Energy-Based Models - Applying and Scaling EBMs to Problems

Explore energy-based models in machine learning, covering applications, scaling techniques, and their impact on classification, calibration, and robustness in AI systems.

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

Institute for Advanced Study

Understanding Deep Neural Networks - From Generalization to Interpretability

Explore deep neural networks, focusing on generalization and interpretability. Delve into graph convolutional networks, spectral filtering, and transferability. Examine fundamental questions and relevance mapping in machine learning.

• YouTube
• 1 hour 2 minutes
• On-Demand
• Free Video

VinAI

Faster Saddle-Point Optimization for Solving Large-Scale Markov Decision Processes

Explore advanced techniques for optimizing large-scale Markov decision processes using saddle-point methods. Gain insights into theoretical reinforcement learning and linear programming formulations.

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