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CS221 - Artificial Intelligence: Principles and Techniques

CS221: Artificial Intelligence: Principles and Techniques. Instructors: Prof. Percy Liang and Prof. Dorsa Sadigh, Department of Computer Science, Stanford University. 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. You can find more information about this course, such as lecture slides and syllabus, here. (from Stanfordonline)

Lecture 17 - Logic: First-Order Logic


Go to the Course Home or watch other lectures:

Lecture 01 - Overview
Lecture 02 - Machine Learning: Linear Classifiers, GSD
Lecture 03 - Machine Learning: Features, Neural Networks
Lecture 04 - Machine Learning: Generalization, K-means
Lecture 05 - Search: Dynamic Programming, Uniform Cost Search
Lecture 06 - Search: A*
Lecture 07 - Markov Decision Processes: Value Iteration
Lecture 08 - Markov Decision Processes: Reinforcement Learning
Lecture 09 - Game Playing: Minimax, Alpha-beta Pruning
Lecture 10 - Game Playing: TD Learning, Game Theory
Lecture 11 - Factor Graphs: Constraint Satisfaction Problems
Lecture 12 - Factor Graphs: Conditional Independence
Lecture 13 - Bayesian Networks: Inference
Lecture 14 - Bayesian Networks: Forward-Backward
Lecture 15 - Bayesian Networks: Maximum Likelihood
Lecture 16 - Logic: Propositional Logic
Lecture 17 - Logic: First-Order Logic
Lecture 18 - Deep Learning
Lecture 19 - Conclusion