‍Полный курс по искусственному интеллекту CS221 от университета Stanford Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019 Lecture 1: Overview Lecture 2: Machine Learning 1 - Linear Classifiers, SGD Lecture 3: Machine Learning 2 - Features, Neural Networks Lecture 4: Machine Learning 3 - Generalization, K-means Lecture 5: Search 1 - Dynamic Programming, Uniform Cost Search Lecture 6: Search 2 - A* Lecture 7: Markov Decision Processes - Value Iteration Lecture 8: Markov Decision Processes - Reinforcement Learning Lecture 9: Game Playing 1 - Minimax, Alpha-beta Pruning Lecture 10: Game Playing 2 - TD Learning, Game Theory Lecture 11: Factor Graphs 1 - Constraint Satisfaction Problems Lecture 12: Factor Graphs 2 - Conditional Independence Lecture 13: Bayesian Networks 1 - Inference Lecture 14: Bayesian Networks 2 - Forward-Backward Lecture 15: Bayesian Networks 3 - Maximum Likelihood Lecture 16: Logic 1 - Propositional Logic Lecture 17: Logic 2 - First-order Logic Lecture 18: Deep Learning Lecture 19: Conclusion www.youtube.com/playlis…playlist 👉 @bigdata_1