Lectures related to Alpha Go: Machine Learning and Game Strategies

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.

General Introduction into Artificial Neural Networks: part 3

Covers learning by rewards in deep reinforcement learning without math details.

First steps toward deep reinforcement learning

Explores the shift to deep reinforcement learning through neural networks for direct policy learning, bypassing Q-values and V-values.

Deep Q-Learning: DeepRL1.1

Covers Deep Q-learning in deep neural networks, its application in games, backpropagation, Q-values, and V-values.

Landscape and Generalisation in Deep Learning

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Chess Positions

Explores different chess positions and historical battle tactics, ending with a musical performance.

Creativity in Science

Explores the essence of science, creativity, and problem-solving through personal reflections and illustrative examples.

Positions in Chess: Understanding Strategies

Explores different chess positions and strategic gameplay, accompanied by musical interpretations of library books.

Introduction to Machine Learning

Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.

Autonomous Vehicles: Intelligence and Perception

Explores intelligence, perception, and AI applications in autonomous vehicles, emphasizing rational thinking and social intelligence.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Deep Learning: Fundamentals

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

Collective Learning Dynamics: Similarity Exploitation

Delves into collective learning dynamics with similarity exploitation, covering structured learning, adaptive frameworks, modeling, simulation, and experimental results.

Generative AI and Reinforcement Learning: Future Directions

Explores advancements in generative AI and reinforcement learning, focusing on their applications, safety, and future research directions.

Introduction to Reinforcement Learning: Concepts and Applications

Introduces reinforcement learning, covering its concepts, applications, and key algorithms.

Artificial Intelligence: The New Radical Addition to Work

Explores the convergence of IoT, Big Data, and Deep Learning in Artificial Intelligence applications across various industries.

Neuroscience and AI: Bridging the Gap

Explores the gap between AI and human intelligence through neuroscience-inspired models and algorithms.

Monte Carlo Tree Search and Alpha Zero

Explores Monte Carlo Tree Search and Alpha Zero in deep reinforcement learning.

Multistage Games: Extensive Form and Feedback Strategies

Explores multistage games, focusing on extensive form and feedback strategies, including Nash equilibria and backward induction methods.

Machine Learning at EPFL

Explores the EPFL Alice unit's role in shaping machine learning and AI in Europe, focusing on research advancements and collaboration within the AI community.