Alpha Go: Machine Learning and Game Strategies

This lecture covers the historical perspective of AI in games, from Deep Blue defeating Garry Kasparov in chess to AlphaGo's victory over top Go players. It explains the concepts of machine learning, expert systems, and deep learning, focusing on the development of the AlphaGo algorithm. The lecture delves into the formulation of proxy functions, building territories in Go, and the AlphaGo Zero algorithm, which mastered Go without human knowledge. It also discusses the use of CNNs in game representations and the reinforcement learning process to optimize network parameters.