Social And Emotional Learning Courses

Bolei Zhou

Towards Falsifiable Interpretability Research in Machine Learning - Lecture

Explore techniques for falsifiable interpretability in machine learning, focusing on saliency, individual neurons, and populations. Learn to build stronger hypotheses for more robust research.

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

Bolei Zhou

Introduction to Reinforcement Learning - Distributed RL Systems - Lecture 9

强化学习分布式系统概述：探讨系统架构、参数更新方法、并行化策略，以及从DQN到AlphaStar的案例研究，深入分析分布式RL系统的发展和实现。

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

Bolei Zhou

Introduction to Reinforcement Learning - Lecture 1: Overview of RL

强化学习入门概述:介绍RL基本概念、特点和应用,探讨其与监督学习的区别,展示RL在游戏、机器人等领域的成功案例,解释RL与深度学习的结合。

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

Bolei Zhou

Interpretable Representation Learning for Visual Intelligence

Explore interpretable representation learning in deep neural networks for visual intelligence, covering object classification, scene recognition, and network visualization techniques.

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

Simons Institute

What Is the Statistical Complexity of Reinforcement Learning?

Explore statistical complexity in reinforcement learning, covering supervised learning, linear models, evaluation challenges, and realizability conditions.

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

Simons Institute

Learning Decentralized Policies in Multiagent Systems - How to Learn Efficiently

Explore decentralized policy learning in multiagent systems, focusing on scalable actor-critic methods, network structures, and performance characterization using stochastic game frameworks.

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

Simons Institute

What Does Machine Learning Offer Game Theory - And Vice Versa

Explore the intersection of machine learning and game theory, focusing on simplifying complex scenarios and building cooperative AI agents for real-world applications.

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

Simons Institute

What Is the Statistical Complexity of Reinforcement Learning?

Explore statistical complexity in reinforcement learning, focusing on conditions for generalization and sample-efficient learning. Introduces the Decision-Estimation Coefficient as a key complexity measure.

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

Simons Institute

V-Learning - Simple, Efficient, Decentralized Algorithm for Multiagent RL

Explore efficient decentralized algorithms for multiagent reinforcement learning, focusing on V-learning's ability to overcome challenges in joint action spaces and achieve Nash equilibria.

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

Simons Institute

Stability and Learning in Repeated Games

Explore stability in repeated games, focusing on traffic routing and online auctions. Learn about no-regret learning, its limitations, and the impact of strategic user behavior on game outcomes.

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

Simons Institute

The Science of Cause and Effect - From Deep Learning to Deep Understanding

Explore the foundations of causal reasoning and its impact on AI, decision-making, and scientific understanding with UCLA's Judea Pearl, a Turing Award winner and pioneer in probabilistic reasoning.

• YouTube
• 1 hour 51 minutes
• Self-Paced
• Free Video

Simons Institute

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

Simons Institute

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

Simons Institute

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

Simons Institute

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