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.
All lecture recordings are posted to Kaltura. This link will work only if you are signed into your UC Berkeley bCourses (Canvas) account.
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 Recording Solutions |
HW0 - Math Diagnostic
Electronic
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 Note 1 | Exam Prep 1 Recording Solutions | |||
2 | Jan 25 | 3 - Informed Search: A* and Heuristics [pdf] [pptx] | Ch. 3.5 - 3.6 | Section 2 Recording Solutions |
HW1 - Search
Electronic
Written
LaTeX template
Solutions
due Wed, Feb 2, 10:59 pm. |
Project 1 due Thu, Feb 3, 10:59 pm. |
Jan 27 | 4 - Local Search [pdf] [pptx] | Ch. 4.1 - 4.2 Note 2 | Exam Prep 2 Recording Solutions | |||
3 | Feb 1 | 5 - Games: Trees, Minimax, Pruning [pdf] [pptx] | Ch. 5.1 - 5.3 Note 3 | Section 3 Recording Solutions |
HW2 - Games
Electronic
Written
LaTeX template
Solutions
due Wed, Feb 9, 10:59 pm. |
Project 2 due Mon, Feb 14, 10:59 pm. |
Feb 3 | 6 - Games: Expectimax, Monte Carlo Tree Search [pdf] [pptx] | Ch. 5.4 - 5.5 | Exam Prep 3 Recording Solutions | |||
4 | Feb 8 | 7 - Propositional Logic and Planning [pdf] [pptx] | Ch. 7.1 - 7.4 Note 4 | Section 4 Recording Solutions |
HW3 - Logic
Electronic
Written
LaTeX template
Solutions
due Fri, Feb 18, 10:59 pm. |
|
Feb 10 | 8 - Logical Inference, Theorem Proving [pdf] [pptx] | Ch. 7.5 - 7.7 | Exam Prep 4 Recording Solutions | |||
5 | Feb 15 | 9 - Boolean Satisfiability, DPLL | Ch. 8.1 - 8.2 | Section 5 Recording Solutions |
HW4 - Probability Review Electronic Written LaTeX template Solutions due Wed, Feb 23, 10:59 pm. |
Project 3 due Fri, Feb 25, 10:59 pm. |
Feb 17 | 10 - First Order Logic [pdf] [pptx] | skim Ch. 9.1 - 9.4 | Exam Prep 5 Recording Solutions | |||
6 | Feb 22 | 11 - Probability Review, Bayesian Networks [pdf] [pptx] | Ch. 13.1 Note 5 | Section 6 Recording Solutions | HW5 - Bayesian Networks
Electronic
Written
LaTeX template
Solutions
due Fri, Mar 4, 10:59 pm. |
|
Feb 24 | 12 - Bayes Nets: Syntax and Semantics [pdf] [pptx] | Ch. 13.2 | Exam Prep 6 Recording Solutions | |||
7 | Mar 1 | 13 - Bayes Nets: Variable Elimination [pdf] [pptx] | Ch. 13.3 | Section 7 Recording Solutions | Study for Midterm | |
Mar 3 | 14 - Bayes Nets: Sampling [pdf] [pptx] | Ch. 13.4 | Midterm Review: SearchSolutions, GamesSolutions, LogicSolutions, Bayes NetsSolutions |
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8 | Mar 8 Midterm 8-10pm Past Exams | 15 - Markov Chains, HMMs [pdf] [pptx] | Ch. 14.1 - 14.2 Note 6 | Section 8 Recording Solutions |
HW6 - Markov Models Electronic Written LaTeX template Solutions due Fri, Mar 18, 10:59 pm. |
|
Mar 10 | 16 - Forward Algorithm, Viterbi Algorithm | Ch. 14.3 - 14.5 | Exam Prep 8 Recording Solutions | |||
9 | Mar 15 | 17 - Dynamic Bayes Nets, Particle Filtering. Utility Theory, Rationality, Decisions [pdf] [pptx] | Ch. 16.1 - 16.3 Note 7 | Section 9 Solutions |
HW7 - Utilities, Decision Nets, VPI Electronic Written LaTeX template Solutions due Wed, Mar 30, 10:59 pm. |
Project 4 due Fri, April 1, 10:59 pm. |
Mar 17 | 18 - Decision Networks and VPI [pdf] [pptx] | Ch. 16.5 - 16.7 | Exam Prep 9 Recording Solutions | |||
10 | Mar 22 | Spring Break | ||||
Mar 24 | Spring Break | |||||
11 | Mar 29 | 19 - Markov Decision Processes: States, Values, Policies, Q-values [pdf] | Ch. 17.1 Note 8 | Section 11 Recording Solutions |
HW8 - MDPs Electronic Written LaTeX template Solutions due Wed, Apr 6, 10:59 pm. |
|
Mar 31 | 20 - MDPs: Dynamic Programming [pdf] | Ch. 17.2 | Exam Prep 11 Recording Solutions | |||
12 | Apr 5 | 21 - Machine Learning I [pdf] | Ch 19.1 - 19.3 Note 9 | Section 12 Recording Solutions |
HW9 - Machine Learning Electronic Written LaTeX template Solutions due Fri, Apr 15, 10:59 pm. |
Project 5 due Fri, April 22, 10:59 pm. |
Apr 7 | 22 - Machine Learning II | Ch. 20.1 - 20.6 | Exam Prep 12 Recording Solutions | |||
13 | Apr 12 | 23 - Neural Networks [pdf] [pptx] | Ch. 21.1 - 21.5 Note 10 | Section 13 Recording Solutions |
HW10 - Neural Networks Electronic Written LaTeX template Solutions due Fri, Apr 22, 10:59 pm. |
|
Apr 14 | 24 - Reinforcement Learning [pdf] [pptx] | Ch. 22.1 - 22.6 Note 11 | Exam Prep 13 Recording Solutions | |||
14 | Apr 19 | Advanced Topics 1: Deep RL and AI Robotics (Mostafa Rohaninejad) | Section 14 Recording Solutions |
HW11 - RL Electronic Written LaTeX template Solutions due Fri, Apr 29, 10:59 pm. |
Project 6 due Fri, April 29, 10:59 pm. |
|
Apr 21 | Advanced Topics 2: Adversarial Deep Learning (Nicholas Carlini) | Exam Prep 14 Recording Solutions | ||||
15 | Apr 26 | Advanced Topics 3: Program Synthesis via Learning (Xinyun Chen) | Section 15 Recording Solutions |
Mini-Contest due Wed, May 4, 10:59 pm. |
||
Apr 28 | Advanced Topics 4: AI Ethics [pdf] [pptx] | Exam Prep 15 Recording Solutions | ||||
16 | May 3 | RRR |
Search Solutions Games Solutions Logic Solutions Bayes Nets Solutions HMMs/VPI Solutions MDPs/RL Solutions Machine learning Solutions |
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May 5 | RRR | |||||
17 | Mon, May 9 | Final Exam: Mon, May 9, 11:30am - 2:30pm | Past Exams |