CS 188 | Introduction to Artificial Intelligence

Spring 2021

Lectures: Mon/Wed/Fri 3:00–3:59 pm, Online

CS188 Robot Waving


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 decision-theoretic 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 and Q&A 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. The recordings are also available on Kaltura, which is a service that UC Berkeley partners with that facilitates the cloud recordings of Zoom meetings. All recordings on Kaltura have automatically-generated captions available by default alongside some other useful controls, such as playback speed adjustment.

To access the channel with recordings for this course, please go to this website and create an account if you don’t have one already: https://kaltura.berkeley.edu

Once you have the account, you should be able to access and subscribe to videos in the channel by following this link.


W Date Lecture Topic Readings Section Homework Project
0 W 1/20 Intro to AI [pdf] [pptx] [lecture] Ch. 1
N/A HW0 - Math, diagnostic
due 1/25 10:59 pm
Project 0
due 1/22 11:59pm
F 1/22 Agents + Environments, part 1 [pdf] [pptx] [lecture] [q&a] Ch. 2
1 M 1/25 Agents + Environments, part 2 [pdf] [pptx] [lecture] Ch. 2
Section 1, solutions,
Exam prep 1, solutions,
HW1 - Uninformed search
due 2/1 10:59 pm

Written HW1 - Probability, uninformed search, and heuristics
due 2/10 10:59 pm on gradescope.

Piazza post on WHW1 self-grading, due 2/22 10:59 pm. Gradescope link.
Project 1
due 2/5 11:59 pm
W 1/27 Uninformed Search [pdf] [pptx] [lecture] [supplementary lecture] [q&a] Ch. 3.1-4
F 1/29 Informed Search [pdf] [pptx] [lecture] [supplementary lecture] [q&a] Ch. 3.5-6
Note 1
2 M 2/1 Local Search [pdf] [pptx] [lecture] [supplementary lecture] [q&a] Ch. 4.1-2
Section 2, solutions,
Exam prep 2, solutions,
HW2 - Informed search and game trees
due 2/8 10:59 pm
W 2/3 Games: minimax, alpha-beta [pdf] [pptx] [lecture] [q&a] Ch. 5.1-3
F 2/5 Games: MCTS, chance [pdf] [pptx] [lecture] [q&a] Ch. 5.4-5
Note 2
3 M 2/8 Propositional Logic [pdf] [pptx] [lecture] [q&a] Ch. 7.1-4
Section 3, solutions,
Exam prep 3, solutions,
HW3 - Propositional logic and local search
due 2/16 10:59 pm

Written HW2 - Games and logic
due 2/24 10:59 pm on gradescope.

Piazza post on WHW2 self-grading, due 3/8 10:59 pm.
Project 2
due 2/19 11:59 pm
W 2/10 Logical Inference: theorem proving, model checking [pdf] [pptx] [lecture] [q&a] Ch. 7.5-6
F 2/12 Propositional Planning [pdf] [pptx] [lecture] [q&a] Ch. 7.7
4 M 2/15 Presidents' Day Section 4, solutions,
Exam prep 4, solutions,
HW4 - First order logic and probability
due 2/22 10:59 pm
W 2/17 FOL: Syntax, Semantics, Inference [pdf] [pptx] [lecture] [q&a] Ch. 8.1-2, skim 9.1-4
Note 3
F 2/19 Probability, Independence, Naive Bayes [pdf] [pptx] [lecture] [q&a] Ch. 12.1-6
5 M 2/22 Bayes nets: syntax, semantics, examples [pdf] [pptx] [lecture] [q&a] Ch. 13.1-2 Section 5, solutions,
Exam prep 5, solutions,
HW5 - Probability and Bayes Nets
due 3/2 10:59 pm
W 2/24 Bayes nets: exact inference [pdf] [pptx] [lecture] [q&a] Ch. 13.3
F 2/26 Bayes nets: stochastic inference (rejection, importance) [pdf] [pptx] [lecture] [q&a] Ch. 13.4
6 M 3/1 Bayes nets: stochastic inference (Gibbs) [pdf] [pptx] [lecture] [q&a] [supp lecture pdf] [supp lecture pptx] [supp lecture recording] Ch. 13.4
Note 4
Section 6, solutions,
Exam prep 6, solutions,
HW6 - Bayes Net Sampling and HMMs
due 3/8 10:59 pm

Written HW3 - Bayes nets and HMMs
due 3/19 10:59 pm on gradescope: links for written component and coding component.

Piazza post on WHW3 self-grading, due 4/5 10:59 pm.
Project 3
due 3/15 11:59 pm
W 3/3 Markov models and filtering [pdf] [pptx] [lecture] [q&a] Ch. 14.1-2
F 3/5 Inference in Markov models [pdf] [pptx] [lecture] [q&a] Ch. 14.3
Note 5
7 M 3/8 DBNs, particle filtering [pdf] [pptx] [lecture] [q&a] Ch. 14.5 Search review, solutions,
Games review, solutions,
Logic review, solutions,
Bayes nets review, solutions,
HMMs review, solutions.

Piazza post with recordings of review sessions
W 3/10 Midterm 5-7 pm PT  
F 3/12 Rationality, utility theory [pdf] [pptx] [lecture] Ch. 16.1-3
8 M 3/15 Decision nets, VPI, unknown preferences [pdf] [pptx] [lecture] Ch. 16.5-7
Note 6
Section 8, solutions,
Exam prep 8, solutions,
HW7 - Utility theory and HMMs
due 3/30 10:59 pm
Project 4
due 4/2 11:59 pm
W 3/17 MDPs: V/Q/pi [pdf] [pptx] [lecture] Ch. 17.1
F 3/19 MDPs: Value/Policy Iter (part 1) [pdf] [pptx] [lecture] Ch. 17.2
9 M 3/22 Spring Break
W 3/24 Spring Break
F 3/26 Spring Break  
10 M 3/29 MDPs: Value/Policy Iter (part 2) [pdf] [pptx] [lecture] Ch. 17.2
Note 7
Section 9, solutions,
Exam prep 9, solutions,
HW8 - MDPs
due 4/5 10:59 pm
W 3/31 ML: Decision Trees (1) [pdf] [pptx] [lecture] Ch. 19.1-3
F 4/2 ML: Decision Trees (2) [pdf] [pptx] [lecture] Ch. 19.1-3
11 M 4/5 ML: Linear Regression and Perceptrons [pdf] [pptx] [lecture] Ch. 19.6 Section 10, solutions,
Exam prep 10, solutions,
HW9 - Machine learning
due 4/13 10:59 pm
Project 5
due 4/16 11:59 pm
W 4/7 ML: Statistical Learning, Naïve Bayes [pdf] [pptx] [lecture] Ch. 20.1-2
Note 8
F 4/9 No lecture
12 M 4/12 ML: Neural Networks (Part 1) [pdf] [pptx] [lecture] Ch. 21.1-5 Section 11, solutions,
Exam prep 11, solutions,
HW10 - Gradient descent and reinforcement learning
due 4/22 10:59 pm

Written HW4 - Machine learning and reinforcement learning
due 4/28 10:59 pm on gradescope: links for written component and coding component.

Piazza post on WHW4 self-grading, due 5/5 10:59 pm
W 4/14 ML: Neural Networks (Part 2) [pdf] [pptx] [lecture] Ch. 21.1-5
Note 9
F 4/16 No lecture
13 M 4/19 Reinforcement Learning I [pdf] [pptx] [lecture] Ch. 22.1-3 Section 12, solutions,
Exam prep 12, solutions,
HW11 - Reinforcement learning
due 4/27 10:59 pm
Project 6
due 4/30 11:59 pm
W 4/21 Reinforcement Learning II [pdf] [pptx] [lecture] Ch. 22.5, 22.7
Note 10
F 4/23 Advanced topics I - Nicholas Carlini on Adversarial Machine Learning [pdf] [lecture] [q&a]
14 M 4/26 Advanced topics II - Moritz Hardt on Fairness and Machine Learning: Limitations and Opportunities [pdf] [pptx] [lecture] [full presentation]   Section 13, solutions,
Exam prep 13, solutions,
W 4/28 Advanced topics III - Jong Wook Kim on CLIP: Learning Transferrable Vision Models from Natural Language Supervision [pdf] [lecture]  
F 4/30 Future + wrapup [pdf] [pptx] [lecture]  
15 M 5/3 RRR   Search review, solutions,
Games review, solutions,
Logic review, solutions,
Bayes nets review, solutions,
HMMs/VPI review, solutions,
MDPs/RL review, solutions,
Machine learning review, solutions

Piazza post with recordings of review sessions
W 5/5 RRR
F 5/7 RRR  
16 M 5/10    
W 5/12 Final 7-10 pm PT
F 5/14