Simons Institute Courses

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
• Self-Paced
• Free Video

Neural Networks and Spurious Correlations

Explore neural networks' vulnerability to spurious correlations and strategies to enhance robustness, including reweighting, subsampling, and overcoming simplicity bias in machine learning models.

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

Causality and Autoencoders in Light of Drug Repurposing for COVID-19

Explore causal inference and autoencoders in drug repurposing for COVID-19, covering observational data, causality hierarchy, and predicting interventions with Caroline Uhler from MIT.

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

Machine Learning-Based Design of Proteins

Explore machine learning-based protein design with Jennifer Listgarten, covering directed evolution, predictive models, and optimization techniques for gene therapy applications.

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

Multi-Agent Reinforcement Learning - Part II

Explore advanced concepts in multi-agent reinforcement learning, including equilibrium, rewards, and techniques like Q-value and no-regret learning. Gain insights into challenges and applications in AI and game theory.

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

Multi-Agent Reinforcement Learning - Part I

Explore multi-agent reinforcement learning, covering game theory, interaction protocols, and Nash equilibrium. Gain insights into challenges and formulations in this emerging field.

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

Reinforcement Learning - Part I

Explore reinforcement learning fundamentals, including credit assignment, dynamic programming, and exploration algorithms. Gain insights into algorithm design and sample complexity in this comprehensive introduction.

• YouTube
• 1 hour 12 minutes
• Self-Paced
• Free Video

Min-Max Optimization - Part III

Explore advanced min-max optimization concepts, applications, and theoretical foundations with MIT expert Constantinos Daskalakis in this comprehensive lecture.

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

Min-Max Optimization - Part II

Explore advanced min-max optimization techniques, including convergence analysis, implicit methods, and applications to multiplayer games and polynomial-type problems.

• YouTube
• 1 hour 9 minutes
• Self-Paced
• Free Video

Min-Max Optimization - Part I

Explore min-max optimization, equilibrium computation, and adversarial gradient descent in zero-sum games, connecting classical techniques to deep learning applications.

• YouTube
• 1 hour 3 minutes
• Self-Paced
• Free Video

Introduction to Causal Graphical Models - Graphs, D-Separation, Do-Calculus

Explore causal graphical models, d-separation, and do-calculus. Learn about graphs, Bayesian networks, and instrumental variables for understanding causality in complex systems.

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

Equivariant Machine Learning Structured Like Classical Physics

Explore equivariant machine learning and its connection to classical physics, focusing on graph neural networks, spectral methods, and symmetry in optimization problems.

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

Equivariant RL

Explore equivariant reinforcement learning, its advantages, and applications in graph neural networks with Max Welling from the University of Amsterdam.

• YouTube
• 1 hour 9 minutes
• Self-Paced
• Free Video

Making Predictions in a Quantum World - Quantum Colloquium

Explore quantum predictions: efficient learning methods, impossible problems, and how quantum technology enhances our predictive capabilities in physics and chemistry.

• YouTube
• 1 hour 2 minutes
• Self-Paced
• Free Video

Yes, Generative Models Are The New Sparsity

Explore how generative models revolutionize data compression and signal processing, offering new perspectives on sparsity and optimization in machine learning.

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