The Syllabus of AI in 2017

(Slides can be downloaded at )

1) Introduction

2) Problem-solving by Search: Minimax Search, Alpha-Beta Search, Stochastic Search Multi-armed bandits, Upper Confidence Bound (UCB), Upper Confidence Bounds on Trees, Monte-Carlo Tree Search(MCTS)

3) Knowledge, Reasoning and Knowledge-base Population: Propositional Reasoning, Predicate Logic Reasoning, Knowledge-base population

4) Statistical Learning and Modeling: Probability theory and distribution,Linear model for regression and classification,Kernel methods and Support vector machines, Bagging method and Boosting method, Decision Tree, K-means Clustering and GMM & Expectation–Maximization (EM) algorithm

5) Deep Learning: Basic concepts and representative models, Deep learning in NLP and visual understanding

6) Deep Reinforcement Learning