Harvard CS50’s Artificial Intelligence with Python – Full University Course

Informed Search: Greedy Best-First and A*00:54:11

Introduction to informed search using heuristics. Explanation of Greedy Best-First Search versus A* search, emphasizing how A* combines path cost (g(n)) and heuristic estimates (h(n)) to find optimal solutions.

Adversarial Search and Minimax01:14:11

Introduction to adversarial game theory and the Minimax algorithm for two-player zero-sum games, detailing how players maximize or minimize utility to make optimal decisions.

Course Overview and Introduction00:00:00

Brian Yu introduces the scope of the course, including search algorithms, knowledge representation, probabilistic reasoning, optimization, machine learning, neural networks, and natural language processing.

Search Problems in AI00:05:40

Introduction to search problems, defining key concepts like agents, states, initial state, actions, transition models, goal tests, and path costs, using examples like the 15 puzzle and map navigation.

Solving Search Problems00:17:18

Explanation of the node data structure, the frontier, and the basic algorithm for searching through a state space, including strategies for tracking explored nodes to prevent infinite loops.

Uninformed Search: DFS and BFS00:28:23

Detailed comparison of Depth-First Search (stack-based) and Breadth-First Search (queue-based), discussing their differences in exploration strategy, memory usage, and optimality.