The Syllabus of AI in 2018 (Wednesday, March-July, three classes/week )

1) Introduction (1 week): Introduction

2) Problem-solving by search(4 weeks):Uninformed Search and Informed (Heuristic) Search, Adversarial Search: Minimax Search, Evaluation Functions, Alpha-Beta Search, Stochastic Search, Adversarial Search: Multi-armed bandits, Upper Confidence Bound (UCB),Upper Confidence Bounds on Trees, Monte-Carlo Tree Search(MCTS) 

3) Statistical learning and modeling (5 weeks): Probability Theory, Model selection, The curse of Dimensionality, Decision Theory, Information Theory, Probability distribution, Linear model for regression, The Bias-Variance Decomposition, Linear model for classification, K-means Clustering and GMM & Expectation–Maximization (EM) algorithm, Boosting

4) Deep Learning (4 weeks):Stochastic Gradient Descent, Backpropagation, Feedforward Neural Network, Convolutional Neural Networks, Recurrent Neural Network  (LSTM, GRU),Generative adversarial network (GAN),Deep learning in NLP (word2vec), CV (localization) and VQA(cross-media)

5) Reinforcement learning (1 week) 

6) Review (1 week)