CS 188 | Introduction to Artificial Intelligence

Summer 2022

Lectures: Mon/Tue/Wed/Thu 2:00–3:30 pm, Lewis 100

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.


Week Day Date Lecture Topic Readings Section Homework Project
1 Mon Jun 20 No Instruction (Juneteenth) HW 0
Electronic HW 0
due Fri, Jun 24 at 11:59pm

HW 1
Electronic HW 1
Written HW 1
due Tue, Jun 28 at 11:59pm
Project 0
due Fri, Jun 24 at 11:59pm

Project 1
due Fri, Jul 1 at 11:59pm
Tue Jun 21 1 - Welcome, Intro to AI [pdf] [pptx] [recording] Ch. 1, 2
Wed Jun 22 2 - Search I: Agents, Search Problems, Uninformed Search [pdf] [pptx] [recording] Ch. 3.1 - 3.4 Regular Discussion 1B [prob] [soln] [recording]
Thu Jun 23 3 - Search II: Informed Search (A* and Heuristics) [pdf] [pptx] [recording] Ch. 3.5 - 3.6
Note 1: Search Problems,
Uninformed & Informed Search
Exam Prep Discussion 1B [prob] [soln] [recording]
Fri Jun 24 4 - CSP I: CSP Problems, Backtracking Search, Forward Checking [pdf] [pptx] [recording] Ch. 6.1 - 6.3
2 Mon Jun 27 5 - CSP II: Arc Consistency, Ordering, Local Search [pdf] [pptx] [recording] Ch. 6.4 - 6.5, Ch. 4.1 - 4.2
Note 2: CSPs, Local Search
Regular Discussion 2A [prob] [soln] [recording] HW 2
Electronic HW 2
Written HW 2
due Tue, Jul 5 at 11:59pm
Project 2
due Fri, Jul 8 at 11:59pm
Tue Jun 28 6 - Adversarial Search and Games I: Minimax, Alpha-beta Pruning [pdf] [pptx] [recording] Ch. 5.1 - 5.3 Exam Prep Discussion 2A [prob] [soln] [recording]
Wed Jun 29 7 - Adversarial Search and Games II: Expectimax, MCTS [pdf] [pptx] [recording] Ch. 5.4 - 5.5
Note 3: Games
Regular Discussion 2B [prob] [soln] [recording]
Thu Jun 30 8 - Optional Buffer Lecture (Logic) [pdf] [pptx] [recording] Exam Prep Discussion 2B [prob] [soln] [recording]
3 Mon Jul 4 No Instruction (Independence Day) HW 3
Electronic HW 3
Written HW 3
due Wed, Jul 13 at 11:59pm
Midterm Contest
due Wed, Jul 13 at 11:59pm
Tue Jul 5 9 - Bayes Nets I: Probability Review [pdf] [pptx] [recording] Ch. 13.1-13.5 Exam Prep Discussion 3A [prob] [soln] [recording]
Wed Jul 6 10 - Bayes Nets II: Bayes Nets Representation [pdf] [pptx] [recording] Ch. 14.1, 14.2, 14.4 Regular Discussion 3B [prob1] [soln1] [prob2] [soln2] [recording]
Thu Jul 7 11 - Bayes Nets III: Exact Inference with Variable Elimination [pdf] [pptx] [pptx_annotated] [recording] Ch. 14.4 Exam Prep Discussion 3B [prob] [soln] [recording]
4 Mon Jul 11 12 - Bayes Nets IV: Independence (D-Separation) [pdf] [pptx] [recording] Ch. 14.4 Regular Discussion 4 [prob] [soln] [recording (conceptual)] [recording (worksheet)]
Tue Jul 12 13 - Bayes Nets V: Approximate Inference (Sampling) [pdf] [pptx] [pptx_annotated] [recording] Ch. 14.4, Ch. 14.5
Note 4: Bayes Nets
Exam Prep Discussion 4 [prob] [soln] [recording]
Wed Jul 13 14 - Markov Models I: Markov Chains, HMMs, Forward Algorithm [pdf] [pptx] [recording] Ch. 15.2-15.6 Search Review [prob][soln]
CSPs Review [prob][soln]
Games Review [prob][soln]
Bayes Nets Review [prob][soln]
Thu Jul 14 15 - Markov Models II: Viterbi, Particle Filtering, Dynamic Bayes Nets [pdf] [pptx] [recording] Ch. 15.2-15.6
Note 6: Markov Models
Fri Jul 15 Midterm (on Lectures 1-12), 7pm-9pm, In-person
Past Exams
5 Mon Jul 18 16 - Decision Making I: Utility Theory, Rationality [pdf] [pptx] [recording] Ch. 16.1-16.3 Regular Discussion 5A [prob] [soln] [recording] HW 4
Electronic HW 4
Written HW 4
due Mon, Jul 25 at 11:59pm
Project 3
due Mon, Jul 25 at 11:59pm
Tue Jul 19 17 - Decision Making II: Decision Networks and VPI [pdf] [pptx] [recording] Ch. 16.5-16.7
Note 7: Utility,
Decision Networks, VPI
Exam Prep Discussion 5A [prob] [soln] [recording]
Wed Jul 20 18 - MDPs I: Formulation, Policies, Values, Q-values [pdf] [pptx] [recording] Ch. 17.1-17.3 Regular Discussion 5B [prob] [soln] [recording]
Thu Jul 21 19 - MDPs II: Exact Solution Methods (Value Iteration, Policy Iteration) [pdf] [pptx] [recording] Ch. 17.1-17.3
Note 8: MDPs,
Value Iteration, Policy Iteration
Exam Prep Discussion 5B [prob] [soln] [recording]
6 Mon Jul 25 20 - RL I: Model-based Learning, Direct Evaluation, TD Learning [pdf] [pptx] [recording] Ch. 22.1 - 22.6 Regular Discussion 6A [prob] [soln] [recording] HW 5
Electronic HW 5
Written HW 5
due Mon, Aug 1 at 11:59pm
Project 4
due Mon, Aug 1 at 11:59pm
Tue Jul 26 21 - RL II: Q-learning, Exploration, Approximate Q-Learning [pdf] [pptx] [recording] Ch. 22.1 - 22.6
Note 11: Reinforcement Learning
Exam Prep Discussion 6A [prob] [soln] [recording]
Wed Jul 27 22 - Machine Learning I: Basics, Naive Bayes [pdf] [pptx] [recording] Ch. 20.1-20.2, 18.6.3, 18.8 Regular Discussion 6B [prob] [soln] [recording]
Thu Jul 28 23 - Machine Learning II: Naive Bayes, Perceptron [pdf] [pptx] [recording] Ch. 18.6.3, 18.8
Note 9: Machine Learning 1
Exam Prep Discussion 6B [prob] [soln] [recording]
7 Mon Aug 1 24 - Machine Learning III: Linear Regression, Logistic Regression [pdf] [pptx] [recording] Ch. 21.1 - 21.5 Regular Discussion 7A [prob] [soln] [recording] HW 6
Electronic HW 6
Written HW 6
due Mon, Aug 8 at 11:59pm
Project 5
due Tue, Aug 9 at 11:59pm

Final Contest
due Tue, Aug 12 at 11:59pm
Tue Aug 2 25 - Machine Learning IV: Neural Networks [pdf] [pptx] [recording] Ch. 21.1 - 21.5
Note 10: Machine Learning 2
Exam Prep Discussion 7A [prob] [soln] [recording]
Wed Aug 3 26 - Final Review Lecture [pdf] [pptx] [recording] HMMs Final Review [prob][soln][recording]
Bayes Nets Final Review [prob][soln]
Thu Aug 4 27 - Advanced Topics I - Inverse Reinforcement Learning and AI Safety (Regina Wang) [pdf] [pptx] [recording] RL/Utility Final Review [prob] [soln][recording]
ML Final Review [prob] [soln]
8 Mon Aug 8 28 - Advanced Topics II - AlphaGo [pdf] [pptx] [recording] Search Final Review [prob][soln]
CSPs Final Review [prob][soln]
Games Final Review [prob][soln][recording]
ML Final Review [prob][soln]
RL/Utility Final Review [prob][soln][recording]
Tue Aug 9 29 - Research Frontiers + Course Wrapup (Final Lecture) [pdf] [pptx] [recording]
Wed Aug 10 Final (on Lectures 1-26), 7pm-10pm, In-person