CS 188 | Introduction to Artificial Intelligence
Summer 2022
Lectures: Mon/Tue/Wed/Thu 2:00–3:30 pm, Lewis 100
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.
Syllabus
| 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 |
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| 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 |
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| 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] |
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| 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 |
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| Fri | Jul 15 | Midterm (on Lectures 1-12), 7pm-9pm, In-person
Past Exams |
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| 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] |
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| 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] |
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| 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] |
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| 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 |