Overview Artificial Intelligence Course | Stanford CS221: Learn AI (Autumn 2019) Machine Learning 1 - Linear Classifiers, SGD | Stanford CS221: AI (Autumn 2019) Machine Learning 2 - Features, Neural Networks | Stanford CS221: AI (Autumn 2019) Machine Learning 3 - Generalization, K-means | Stanford CS221: AI (Autumn 2019) Search 1 - Dynamic Programming, Uniform Cost Search | Stanford CS221: AI (Autumn 2019) Search 2 - A* | Stanford CS221: Artificial Intelligence (Autumn 2019) Markov Decision Processes 1 - Value Iteration | Stanford CS221: AI (Autumn 2019) Markov Decision Processes 2 - Reinforcement Learning | Stanford CS221: AI (Autumn 2019) Game Playing 1 - Minimax, Alpha-beta Pruning | Stanford CS221: AI (Autumn 2019) Game Playing 2 - TD Learning, Game Theory | Stanford CS221: Artificial Intelligence (Autumn 2019) Factor Graphs 1 - Constraint Satisfaction Problems | Stanford CS221: AI (Autumn 2019) Factor Graphs 2 - Conditional Independence | Stanford CS221: AI (Autumn 2019) Bayesian Networks 1 - Inference | Stanford CS221: AI (Autumn 2019) Bayesian Networks 2 - Forward-Backward | Stanford CS221: AI (Autumn 2019) Bayesian Networks 3 - Maximum Likelihood | Stanford CS221: AI (Autumn 2019) Logic 1 - Propositional Logic | Stanford CS221: AI (Autumn 2019) Logic 2 - First-order Logic | Stanford CS221: AI (Autumn 2019) Deep Learning | Stanford CS221: AI (Autumn 2019) Conclusion | Stanford CS221: AI (Autumn 2019)