This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decisiontheoretic modeling paradigm.
By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.
See the syllabus for slides, deadlines, and the lecture schedule. Readings refer to fourth edition of AIMA unless otherwise specified.
We make lecture recordings available as links to Google Drive, which you can find posted together with other materials on the Syllabus page of this website shortly after the lecture. These links will work only if you are signed into your UC Berkeley Google account.
If you don't have a UC Berkeley account but want to view CS 188 lectures, we recommend the Fall 2018 website instead.
Slides from the Fall 2020 version of the course have been posted for each lecture at the start of semester, as a reference. After lectures, they will be replaced by updated slides. Similarly, notes have been posted from the Fall 2020 version of the course, and will be updated to reflect actual course content.
W  Date  Lecture Topic  Readings  Section  Homework  Project 

0  Th 8/26  1  Intro to AI [pdf] [pptx] [recording] 
Ch. 1, 2  N/A  HW0  Math, diagnostic (optional) 'due' 8/27 10:59 pm. 
Project 0 due 8/30 10:59 pm. 
1  Tu 8/31  2  Uninformed Search [pdf] [pptx] [recording] 
Ch. 3.14 Note 1  fa20 
Section 1, solutions, walkthrough. Exam prep 1, solutions, walkthrough. 
HW1  Search and Heuristics Main HW, challenge question pdf and submission link, due 9/7 10:59 pm. Challenge question solutions. Challenge question reflection due 9/14 10:59 pm. 
Project 1 due 9/10 10:59 pm. 
Th 9/2  3  UCS, A*, and Heuristics [pdf] [pptx] [recording] 
Ch. 3.56  
2  Tu 9/7  4  Constraint Satisfaction Problems I [pdf] [pptx] [recording] 
Ch. 6.1 Note 2 CSP applet 
Section 2, solutions, walkthrough. Exam prep 2, solutions, walkthrough. 
HW2  Constraint Satisfaction Problems Main HW, challenge question pdf and submission link, due 9/14 10:59 pm. Challenge question solutions. Challenge question reflection due 9/21 10:59 pm. 
MiniContest 1 due 9/24 11:59 pm. 
Th 9/9  5  Constraint Satisfaction Problems II [pdf] [pptx] [recording] 
Ch. 6.25  
3  Tu 9/14  6  Search with Other Agents I [pdf] [pptx] [recording] 
Ch. 5.25 Note 3  fa20 
Section 3, solutions, walkthrough. Exam prep 3, solutions, walkthrough. 
HW3  Games Main HW, challenge question pdf and submission link, due 9/21 10:59 pm. Challenge question solutions. Challenge question reflection due 9/28 10:59 pm. 
Project 2 due 9/24 10:59 pm. 
Th 9/16  7  Search with Other Agents II [pdf] [pptx] [recording] 
Ch. 16.13  
4  Tu 9/21  8  Markov Decision Processes I [pdf] [pptx] [recording] 
17.13 Note 4  fa20 
Section 4, solutions, walkthrough. Exam prep 4, solutions, walkthrough. 
HW4  Markov Decision Processes Main HW, challenge question pdf and submission link, due 9/28 10:59 pm. Challenge question solutions. Challenge question reflection due 10/5 10:59 pm. 
MiniContest 2 due 10/22 11:59 pm. 
Th 9/23  9  Markov Decision Processes II [pdf] [pptx] [recording] 
Ch. 17.13  
5  Tu 9/28  10  Reinforcement Learning I [pdf] [pptx] [recording] 
Ch. 21 Note 5  fa20 
Section 5, solutions, walkthrough. Exam prep 5, solutions, walkthrough. 
HW5  Reinforcement Learning Main HW, challenge question pdf and submission link, due 10/5 10:59 pm. Challenge question solutions. Challenge question reflection due 10/19 10:59 pm. 
Project 3 due 10/8 10:59 pm. 
Th 9/30  11  Reinforcement Learning II [pdf] [pptx] [recording] 
Ch. 21  
6  Tu 10/5  12  Probability [pdf] [pptx] [recording] 
Ch. 13.15 Note 6  fa20 
Midterm review Search worksheet, solutions. CSPs worksheet, solutions. Games worksheet, solutions. MDPs worksheet, solutions. Reinforcement learning worksheet, solutions. 

Th 10/7  13  Bayesian Networks: Representation [pdf] [pptx] [recording] 
Ch. 14.12, 14.4  
7  Tu 10/12  14  Bayesian Networks: Inference [pdf] [pptx] [recording] 
Ch. 14.4  Section 6, solutions, walkthrough. Exam prep 6, solutions, walkthrough. 
HW6  Probability and Bayesian Networks Main HW, challenge question pdf and submission link, due 10/19 10:59 pm. Challenge question solutions. Challenge question reflection due 10/26 10:59 pm. 

Th 10/14  Midterm 79 pm PT  
8  Tu 10/19  15  Bayesian Networks: Independence [pdf] [pptx] [recording] 
Ch. 14.12, 14.4  Section 7, solutions, walkthrough. Exam prep 7, solutions, walkthrough. 
HW7  Bayesian Networks Main HW, challenge question pdf and submission link, due 10/26 10:59 pm. Challenge question solutions. Challenge question reflection due 11/2 10:59 pm. 
Project 4 due 11/12 10:59 pm. 
Th 10/21  16  Bayesian Networks: Sampling [pdf] [pptx] [recording] 
Ch. 14.45  
8  Tu 10/26  17  Decision Networks and VPI [pdf] [pptx] [recording] 
Ch. 16.56 Note 7  fa20 
Section 8, solutions, walkthrough. Exam prep 8, solutions, walkthrough. 
HW8  Decision Networks, Hidden Markov Models, and Particle Filtering Main HW, challenge question pdf and submission link, due 11/9 10:59 pm. Challenge question solutions. Challenge question reflection due 11/16 10:59 pm. 

Th 10/28  18  Hidden Markov Models [pdf] [pptx] [recording] 
Ch. 15.26 Note 8  fa20 

10  Tu 11/2  19  Particle filtering [pdf] [pptx] [recording] 
Ch. 15.26  Section 9, solutions, walkthrough. Exam prep 9, solutions, walkthrough. 

Th 11/4  20  Machine Learning: Naïve Bayes [pdf] [pptx] [recording] 
Ch. 20.12 Note 9  fa20 

11  Tu 11/9  21  Machine Learning: Perceptrons and Logistic Regression [pdf] [pptx] [recording] 
Ch. 18.6.3, 18.8  Section 10, solutions, walkthrough. Exam prep 10, solutions, walkthrough. 
HW9  HMMs and Machine Learning Main HW, challenge question pdf and submission link, due 11/16 10:59 pm. Challenge question solutions. Challenge question reflection due 11/23 at 10:59 pm. 

Th 11/11  Veterans Day  
12  Tu 11/16  22  Machine Learning: Optimization [pdf] [pptx] [recording] 
Ch. 18.6.3, 18.8 Note 10  fa20 
Section 11, solutions, walkthrough. Exam prep 11, solutions, walkthrough. 
HW10  Machine Learning Main HW, challenge question pdf and submission link, due 11/30 10:59 pm. Challenge question solutions. Challenge question reflection due 12/9 at 10:59 pm. 
Project 5 due 12/3 10:59 pm. 
Th 11/18  23  Machine Learning: Neural Networks [pdf] [pptx] [recording] 

13  Tu 11/23  Thanksgiving break  
Th 11/25  Thanksgiving break  
14  Tu 11/30  24  Advanced Applications: Games and Robotics [pdf] [pptx] [recording] 
Finals review Search and CSPs, solutions Games and utilities, solutions MDPs and RL, solutions Bayes nets, solutions HMMs, solutions ML I  Naïve Bayes, Perceptron, and Logistic Regression, solutions ML II  Optimization and Neural Networks, solutions 

Th 12/2  25  Conclusion [pdf] [pptx] [recording] 

15  Tu 12/7  RRR  
Th 12/9  RRR  
16  Tu 12/14  N/A (Finals Week)  
Th 12/16  Final 11:30 am  2:30 pm PT 