Stanford University Artificial Intelligence Program
Semester : Spring 2018
pre-knowledge : This course requires students to have some basic programming, discrete mathematics, and probability theory.
What do web search, speech recognition, face recognition, machine translation, autonomous driving and automated scheduling have in common? They are all complex real-world problems, and it is the goal of AI to solve them using well-thought-out mathematical tools. In this course, you will learn the fundamentals of these applications and the practical implementation of some of these systems. Specifically, this course will cover machine learning, search, games, Markov decision processes, constraint satisfaction, graph models, and logic. The main goal of the course is for students to master these techniques and be able to solve new AI problems in the future.
The course is divided into modules on Introduction, Machine Learning, Search, MDP, Gaming, Constraint Satisfaction, Bayesian Networks, and Logic Foundations. Each module contains the following.