Simons Institute Courses

Generalization and Equilibrium in Generative Adversarial Nets - GANs

Explore the theoretical foundations of GANs, focusing on generalization and equilibrium concepts in representation learning with Princeton's Sanjeev Arora.

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

Evaluating Neural Network Representations Against Human Cognition

Explore neural network representations and human cognition, comparing categorization models, psychological spaces, and relational similarities to enhance understanding of cognitive processes.

• YouTube
• 1 hour 4 minutes
• Self-Paced
• Free Video

The Missing Signal

Explore representation learning and its applications in AI research with insights from Facebook's leading expert.

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

Geometry, Optimization and Generalization in Multilayer Networks

Explore the interplay of geometry, optimization, and generalization in multilayer networks, focusing on representation learning and its applications in deep learning architectures.

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

Tutorial on Deep Learning IV

Explore advanced deep learning concepts with Carnegie Mellon expert Ruslan Salakhutdinov, covering cutting-edge techniques and applications in machine learning.

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

Tutorial on Deep Learning III

Explore advanced deep learning concepts and techniques with Carnegie Mellon expert Ruslan Salakhutdinov in this comprehensive tutorial from the Foundations of Machine Learning Boot Camp.

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

Tutorial on Deep Learning I

Explore deep learning foundations, from neural networks to advanced techniques, with insights on feature representation, optimization, and practical applications in computer vision and audio processing.

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

Nonparametric Bayesian Methods - Models, Algorithms, and Applications IV

Explore probabilistic models for graphs, edge exchangeability, and graph paintbox representations in this advanced machine learning lecture on nonparametric Bayesian methods.

• YouTube
• 1 hour 4 minutes
• Self-Paced
• Free Video

Information Thresholds in Structure Estimation

Explore information thresholds in structure estimation, examining theoretical foundations and practical applications in machine learning and data analysis.

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

Privacy and Fairness in Collaborative AI

Explore information-theoretic approaches to enhance privacy and fairness in collaborative AI systems, focusing on trustworthy machine learning techniques.

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

The Limits of Group Fairness and Predictive Multiplicity

Explore information-theoretic approaches to group fairness and predictive multiplicity in machine learning, examining their limitations and implications for trustworthy AI systems.

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

Decision Making with Information-Theoretic Constraints

Explore information-theoretic constraints in decision-making processes, focusing on applications in trustworthy machine learning and their impact on algorithmic outcomes.

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

Fair, Explainable, and Lawful Machine Learning for High-Stakes Applications

Explore information-theoretic methods for trustworthy ML, focusing on fairness, explainability, and legal compliance in critical applications.

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

Variational Formulations and Distributed Convex Optimization Methods for...

Explore variational formulations and distributed convex optimization methods in information-theoretic approaches to trustworthy machine learning.

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

Learning Under Data Poisoning

Explore data poisoning's impact on machine learning, its detection methods, and strategies for building robust models against adversarial attacks in this informative talk.

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