Safe Learning and Control

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (29)

Page 2 of 3

Policy Gradient and Actor-Critic Methods: Eligibility Traces Explained

Discusses policy gradient and actor-critic methods, focusing on eligibility traces and their application in reinforcement learning tasks.

Reinforcement Learning: Q-Learning

Covers Q-Learning, a model-free reinforcement learning algorithm, and its application to Tic-Tac-Toe with examples and quizzes.

Acquiring Data for Learning: Modern Approaches and Challenges

Explores modern approaches and challenges in acquiring data for learning optimal controllers through demonstrations and data-driven methods.

Model-Based Deep Reinforcement Learning: Monte Carlo Tree Search

Explores model-based deep reinforcement learning, focusing on Monte Carlo Tree Search and its applications in game strategies and decision-making processes.

Reinforcement Learning: Basics and Applications

Covers the basics of reinforcement learning, including trial-and-error learning, Q-learning, deep RL, and applications in gaming and planning.

Introduction to Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Introduction to Reinforcement Learning: Key Concepts and Applications

Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.

Neural Networks: Multilayer Learning

Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.

Deep Learning: Fundamentals

Covers the fundamentals of deep learning, from artificial neurons to modern networks.

The Hidden Convex Optimization Landscape of Deep Neural Networks

Explores the hidden convex optimization landscape of deep neural networks, showcasing the transition from non-convex to convex models.