What do web search, speech recognition, face recognition, machine translation, autonomous driving, and automatic scheduling have in common? These are all complex real-world problems, and the goal of artificial intelligence (AI) is to tackle these with rigorous mathematical tools.

In this course, you will learn the foundational principles that drive these applications and practice implementing some of these systems. Specific topics include machine learning, search, game playing, Markov decision processes, constraint satisfaction, graphical models, and logic. The main goal of the course is to equip you with the tools to tackle new AI problems you might encounter in life.

Instructors:

Lecture 1: Overview | Stanford CS221: AI (Autumn 2019)

Topics: Overview of course, Optimization

Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University

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Lecture 2: Machine Learning 1 – Linear Classifiers, SGD | Stanford CS221: AI (Autumn 2019)

Topics: Linear classification, Loss minimization, Stochastic gradient descent

Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University

Lecture 3: Machine Learning 2 – Features, Neural Networks | Stanford CS221: AI (Autumn 2019)

Topics: Features and non-linearity, Neural networks, nearest neighbors

Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University

Lecture 4: Machine Learning 3 – Generalization, K-means | Stanford CS221: AI (Autumn 2019)

Topics: Generalization, Unsupervised learning, K-means

Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University

Lecture 5: Search 1 – Dynamic Programming, Uniform Cost Search | Stanford CS221: AI (Autumn 2019)

Topics: Problem-solving as finding paths in graphs, Tree search, Dynamic programming, uniform cost search

Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University

Lecture 6: Search 2 – A* | Stanford CS221: AI (Autumn 2019)

Topics: Problem-solving as finding paths in graphs, A*, consistent heuristics, Relaxation

Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University

Lecture 7: Markov Decision Processes – Value Iteration | Stanford CS221: AI (Autumn 2019)

Topics: MDP1, Search review, Project

Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University

Lecture 9: Game Playing 1 – Minimax, Alpha-beta Pruning | Stanford CS221: AI (Autumn 2019)

Topics: Minimax, expectimax, Evaluation functions, Alpha-beta pruning

Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University

Lecture 10: Game Playing 2 – TD Learning, Game Theory | Stanford CS221: AI (Autumn 2019)

Topics: TD learning, Game theory

Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University

Source: https://stanford-cs221.github.io/autumn2019/