CS 188 | Introduction to Artificial Intelligence

Spring 2022

Lectures: Tu/Th 2:00–3:30 pm, Wheeler 150 (Online for first two weeks on Zoom)

CS188 Robot Waving

Description

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.

Lecture reference materials

We make lecture recordings available as links to Kaltura, 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 bCourses (Canvas) account.


Syllabus

W Date Lecture Topic Readings Section Homework Project
1 Tuesday, Jan 18 1 - Intro to AI, Rational Agents [pdf] [pptx] Ch. 1, 2 Section 1 HW0 - Math Diagnostic
due Wed, Jan 26, 10:59 pm.
Project 0
due Mon, Jan 24, 10:59 pm.
Thursday, Jan 20 2 - State Spaces, Uninformed Search [pdf] [pptx] Ch. 3.1 - 3.4 Exam Prep 1
2 Jan 25 3 - Informed Search: A* and Heuristics Ch. 3.5 - 3.6 Section 2 HW1 - Search
due Wed, Feb 2
Project 1
due Fri, Jan 28
Jan 27 4 - Local Search Ch. 4.1 - 4.2 Exam Prep 2
3 Feb 1 5 - Games: Trees, Minimax, Pruning Ch. 5.1 - 5.3 Section 3 HW2 - Games
due Feb 9
Project 2
due Feb 11
Feb 3 6 - Games: Expectimax, Monte Carlo Tree Search Ch. 5.4 - 5.5 Exam Prep 3
4 Feb 8 7 - Propositional Logic and Planning Ch. 7.1 - 7.4 Section 4 HW3 - Logic
due Feb 16
Feb 10 8 - Logical Inference, Theorem Proving, DPLL Ch. 7.5 - 7.7 Exam Prep 4
5 Feb 15 9 - First-Order Logic Ch. 8.1 - 8.2, skim Ch. 9.1 - 9.4 Section 5 HW4 - Bayes Nets
due Feb 23
Feb 17 10 - Probability, Bayesian Networks, Naive Bayes Ch. 13.1 - 13.2 Exam Prep 5
6 Feb 22 11 - Bayes Nets: Variable Elimination Ch. 13.3 Section 6 HW5 - Sampling
due Mar 2
Feb 24 12 - Bayes Nets: Structure and Sampling I Ch. 13.4 Exam Prep 6
7 Mar 1 13 - Bayes Nets: Sampling II Ch. 13.4 Midterm Review Study for Midterm
Mar 3 14 - Markov Chains, HMMs, Inference, Forward Algorithm Ch. 14.1 - 14.2
8 Mar 8 15 - Viterbi Algorithm, Particle Filtering, Dynamic Bayes Nets MIDTERM (covers Lectures 1-13) in the evening (7-9pm or 8-10pm) Ch. 14.3 - 14.5 Section 8 HW6 - Markov Models
due Mar 16
Mar 10 16 - Utility Theory, Rationality Ch. 16.1 - 16.3 Exam Prep 8
9 Mar 15 17 - Decision Networks and VPI Ch. 16.5 - 16.7 Section 9 HW7 - Utilities, Decision Nets, VPI
due Mar 30
Mar 17 18 - Markov Decision Processes: States, Policies, Values, Q-values Ch. 17.1 Exam Prep 9
10 Mar 22 Spring Break
Mar 24 Spring Break
11 Mar 29 19 - MDPs: Dynamic Programming Ch. 17.2 Section 11 HW8 - MDPs
due Apr 6
Mar 31 20 - Machine Learning I Ch. 19.1 - 19.3 Exam Prep 11
12 Apr 5 21 - Machine Learning II Ch. 19.6, Ch 20.1 - 20.2 Section 12 HW9 - Machine Learning
due Apr 13
Apr 7 22 - Neural Networks I Ch. 21.1 - 21.5 Exam Prep 12
13 Apr 12 23 - Neural Networks II Ch. 21.1 - 21.5 Section 13 HW10 - Neural Networks
due Apr 20
Apr 14 24 - Reinforcement Learning I Ch. 22.1 - 22.3 Exam Prep 13
14 Apr 19 25 - Reinforcement Learning II Ch. 22.5 - 22.7 Section 14 HW 11 - RL
due Apr 27
Apr 21 26 - Buffer or Advanced Topics   Exam Prep 14
14 Apr 26 27 - Buffer or Advanced Topics Finals Review
Apr 28 28 - Buffer or Advanced Topics  
16 May 3 RRR  
May 5 RRR  
17 May 10 Finals Week  
May 12 Finals Week