Week 1: Introduction to AI, history of AI, course logistics, and roadmap
Week 2: Intelligent agents, uninformed search
Week 3: Heuristic search, greedy search, A* algorithm, stochastic search
Week 4: Adversarial search, game playing
Week 5: Machine Learning 1: basic concepts, linear models, K nearest neighbors, overfitting
Week 6: Machine Learning 2: perceptrons, neural networks, naive Bayes
Week 7: Machine Learning 3: Decision trees, ensemble, logistic regression, and unsupervised learning
Week 8: Constraint satisfaction problems
Week 9: Markov decision processes, reinforcement learning.
Week 10: Logical agents, propositional logic and first order logic
Week 11: AI applications to natural language processing (NLP)
Week 12: AI applications and course review
1) Introduction to AI, history of AI, course logistics, and roadmap