Pascal Poupart Courses

CS885 Reinforcement Learning - Spring 2020

Dive into advanced reinforcement learning techniques including trust region methods, maximum entropy approaches, and imitation learning strategies.

• YouTube
• 1 hour 55 minutes
• Self-Paced
• Free Video

Introduction to Machine Learning - Spring 2019 - University of Waterloo

Dive into comprehensive machine learning fundamentals covering neural networks, SVMs, deep learning, CNNs, transformers, and ensemble methods from University of Waterloo.

• YouTube
• 1 day 4 hours 26 minutes
• Self-Paced
• Free Video

Reinforcement Learning - Spring 2018

Master reinforcement learning algorithms that enable machines to learn from partial feedback, covering Markov processes, deep RL, bandits, and applications in robotics and games.

• YouTube
• 1 day 22 minutes
• Self-Paced
• Free Video

Inverse RL

Explore inverse reinforcement learning, its applications, and key concepts like feature matching and maximizing margins. Learn implementation techniques and policy induction.

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

Distributional RL

Explore distributional reinforcement learning, covering return distribution, policy evaluation, and the C51 algorithm, with insights into its advantages and applications in Atari games.

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

Partially Observable Reinforcement Learning

Explore partially observable reinforcement learning, covering Markov processes, hidden models, and recurrent neural networks for decision-making in uncertain environments.

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

Imitation Learning

Explore imitation learning techniques, from behavioral cloning to generative adversarial methods, with applications in autonomous driving and conversational agents.

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

Maximum Entropy Reinforcement Learning

Explore maximum entropy reinforcement learning, covering key concepts like soft Q-learning and soft actor-critic algorithms, with a focus on encouraging stochasticity in optimal policies.

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

Trust Region & Proximal Policy Optimization

Explore trust region methods and proximal policy optimization in reinforcement learning, focusing on policy gradients, KL divergence, and practical implementations like TRPO and PPO.

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

Normalizing Flows - Priyank Jaini

Explore normalizing flows, density estimation, and autoregressive models. Learn about change of variables, increasing triangular maps, and neural autoregressive flows.

• YouTube
• 1 hour 5 minutes
• Self-Paced
• Free Video

Autoencoders

Explore autoencoders, from basic concepts to advanced applications, covering compression, PCA, deep architectures, sparse representations, and denoising techniques.

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

CS480-680 Lecture 19 - Attention and Transformer Networks

Explore attention mechanisms, transformer networks, and their applications in machine learning, including multihead attention and normalization techniques.

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

CS480-680 - Hidden Markov Models

Explore Hidden Markov Models, their assumptions, and applications in robot localization. Learn about monitoring tasks, hindsight reasoning, and finding the most likely explanation.

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

Convolutional Neural Networks

Explore convolutional neural networks, from basic concepts to advanced architectures, including edge detection, pooling, and feature maps for image recognition tasks.

• YouTube
• 1 hour 15 minutes
• Self-Paced
• Free Video

Support Vector Machines - Continued

Explore advanced concepts of support vector machines, including soft margins, slack variables, and multi-class classification techniques.

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