This is a course for junior students in the School of Data Science, Fudan University. It's mainly adapted from course CS188 at Berkeley. Check out the CS188 website for more materials.

The course mainly covers the following SIX topics.

• Classical Search Algorithms (BFS, DFS, UCS, A*, Adversarial Search)
• Constraint Satisfaction Problem (CSP)
• Bayes' Net and Inference
• Hidden Markov Model (HMM)
• Markov Decision Problems (MDPs)
• Reinforcement Learning (active/negative learning, Q-learning)

There are four Labs during the semester on the following four topics respectively.

• Uniform Cost Search
• alpha-beta pruning in Minimax Search
• MDPs (value/policy iteration)
• Exact inference in Bayes' Net

There are also four mini-projects during the semester on the following four topics respectively.

• Search in Pacman
• CSP in n-queens
• MDPs and Q-learning in blackjack
• HMM and inference

• Gomoku