Simons Institute Courses

Planning and Markov Decision Processes - Part 1

Explore fundamental concepts of planning and Markov Decision Processes, including control objectives, randomizing policies, and basic methods for reinforcement learning theory.

• YouTube
• 1 hour 17 minutes
• On-Demand
• Free Video

Stochastic Programming Approach to Optimization Under Uncertainty - Part 2

Explore advanced optimization techniques for uncertain scenarios, covering dynamic programming, discretization, cutting planes, and multistage problems in reinforcement learning theory.

• YouTube
• 1 hour 9 minutes
• On-Demand
• Free Video

Statistical Considerations in Reinforcement Learning - Part 1a

Explore statistical considerations in reinforcement learning, covering inverse RL, high-stakes problems, and asymptotic frameworks for parameter estimation and uncertainty quantification.

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

The AI Economist- Improving Equality and Productivity with AI-Driven Tax Policies

Explore AI-driven tax policies for improved economic equality and productivity, featuring deep learning, reinforcement learning, and multi-agent simulations in a simplified economic model.

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

Batch Offline Reinforcement Learning - Part 1

Explore offline reinforcement learning theory, covering key concepts, challenges, and evaluation methods for developing AI systems using pre-collected data.

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

Online Learning in Markov Decision Processes - Part 2

Explore advanced concepts in Markov Decision Processes, including adversarial scenarios, regret analysis, and algorithms for online learning in dynamic environments.

• YouTube
• 1 hour 13 minutes
• On-Demand
• Free Video

Online Learning in Markov Decision Processes - Part 1

Explore online learning in Markov Decision Processes, covering theory, algorithms, and optimal strategies for reinforcement learning in dynamic environments.

• YouTube
• 1 hour 26 minutes
• On-Demand
• Free Video

Online Learning and Bandits - Part 2

Explore advanced bandit algorithms, including Exponential Weights, UCB, and Thompson Sampling. Learn design principles and analysis techniques for optimal decision-making in uncertain environments.

• YouTube
• 1 hour 4 minutes
• On-Demand
• Free Video

Online Learning and Bandits - Part 1

Explore online learning and bandit algorithms with experts from DeepMind and CWI. Gain insights into gradient descent, exponential weights, and regularized leader methods for reinforcement learning.

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

Planning and Markov Decision Processes - Part 2

Explore advanced concepts in reinforcement learning, including generative models, sample complexity, value equations, and linear programming, with experts from leading institutions.

• YouTube
• 1 hour 13 minutes
• On-Demand
• Free Video

Recent Progress in High-Dimensional Learning

Explore recent advancements in high-dimensional learning, covering robust estimation, maximum likelihood, and algorithmic approaches for handling noise and adversarial scenarios.

• YouTube
• 1 hour 7 minutes
• On-Demand
• Free Video

Recent Progress in High-Dimensional Learning

Explore recent advancements in high-dimensional learning, covering factor analysis, phylogenetic reconstruction, and hidden Markov models, with applications in genetics and machine learning.

• YouTube
• 1 hour 3 minutes
• On-Demand
• Free Video

Statistical Physics and Computation in High Dimension

Explore high-dimensional statistical physics and computation, covering sampling, minimization, and replica methods with applications in finance and algorithms.

• YouTube
• 1 hour 18 minutes
• On-Demand
• Free Video

Crash Course on Probabilistically Checkable Proofs - PCP

Explore probabilistically checkable proofs, their formulation, global vs local tests, and computational implications in this comprehensive lecture on advanced theoretical computer science concepts.

• YouTube
• 1 hour 18 minutes
• On-Demand
• Free Video

Remarks on the Discrete Cube

Explore probability theory on the discrete cube, covering Fourier analysis, Poisson inequality, and random walks, with applications in high-dimensional geometry and computation.

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