Artificial Intelligence - Principles and Techniques - Autumn 2019

Understand Artificial Intelligence, its principles and techniques with this Standford engineering course.

The course would cover Machine Learning, Search Algorithms, Markov's decision processes, game playing, factor graphs, bayesian networks, logic and deep learning

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).