Artificial Intelligence: Principles and Techniques

CS
221
Instructors
Liang, P. (PI)
Sagawa, S. (TA)
Sadigh, D. (PI)
Fang, F. (TA)
Piseno, M. (TA)
Li, T. (TA)
Liao, M. (TA)
Shen, K. (TA)
Khanna, S. (TA)
Palaparthi, A. (TA)
Yu, K. (TA)
Zhou, Z. (TA)
Gillespie, L. (TA)
Wu, Y. (TA)
Lowe, S. (TA)
Wang, Y. (TA)
Sridhar, A. (TA)
Section Number
1
Artificial intelligence (AI) has had a huge impact in many areas, including medical diagnosis, speech recognition, robotics, web search, advertising, and scheduling. This course focuses on the foundational concepts that drive these applications. In short, AI is the mathematics of making good decisions given incomplete information (hence the need for probability) and limited computation (hence the need for algorithms). Specific topics include search, constraint satisfaction, game playing,n Markov decision processes, graphical models, machine learning, and logic. Prerequisites: CS 103 or CS 103B/X, CS 106B or CS 106X, CS 109, and CS 161 (algorithms, probability, and object-oriented programming in Python). We highly recommend comfort with these concepts before taking the course, as we will be building on them with little review.
Grading
Letter or Credit/No Credit
Units
3-4
Graduate
Course Tags
Computational Policy - Electives
Computational Policy Analysis
Academic Year
Quarter
Autumn
Section Days
Monday Wednesday
Start Time
1:30 PM
End Time
3:00 PM
Location
Remote