Schedule
This schedule is tentative and subject to change. It may be a bit ambitious.
Supplmentary and optional background reading provided when appropriate.
R&N == Aritificial Intelligence: A Modern Approach, Russell and Norvig, 3rd edition.
References for Unit 1 (Logic)
For the unit on logic you may also want to consult:
- Category Theory for the Sciences, Spivak, pdf
- Logic in Computer Science, Huth and Ryan
- Logic for Computer Scientists, Schoening
Huth and Ryan is an excellent introductory text for temporal and epistemic logics, which we will touch on in Unit 3 (agent-based reasoning).
References for Unit 3 (Agents: Belief and Time)
- Advanced Data Analysis from an Elementary Point of View, Part III, Shalizi, pdf
- Logic in Computer Science, Huth and Ryan
| Date | Topic | Form | Deadlines & Notes |
|---|---|---|---|
| Wed, Jan 19 | Intro: What is AI? | Lecture | |
| Fri, Jan 21 | Knowledge Representation | Lecture | R&N: 12.1, 12.2 Set notation cheatsheet NSF Workshop: Research Challenges and Opportunitites in KR |
| Mon, Jan 24 | Propositional Logic | Lecture | Add Deadline R&N: 7.3 |
| Wed, Jan 26 | First Order (Predicate) Logic | Lecture | Theory Assignment 1 out R&N 8.2 |
| Fri, Jan 28 | Logical Inference I | Lecture | R&N 7.5 |
| Mon, Jan 31 | Review of Logical Inference | Michael Q&A | No Teams broadcast today Programming Assignment 1 out |
| Wed, Feb 2 | Logical Inference II and Resolution | Videos | Theory Assignment 1 due (soft) R&N 7.5, 9.5 |
| Fri, Feb 4 | Application: Law and Logic Programming | Lecture | Drop Deadline Theory Assignment 1 due (hard) R&N 9.4 |
| Mon, Feb 7 | Proofs as Planning and Intro to Search | Lecture | R&N 10.1, 10.2 |
| Wed, Feb 9 | Exam 1: Logic | Exam | |
| Fri, Feb 11 | Background: Discrete Probability Theory | Lecture | Programming Assignment 1 due (soft) |
| Mon, Feb 14 | CANCELLED | ||
| Wed, Feb 16 | CANCELLED | ||
| Fri, Feb 18 | Search Agents | Lecture (Michael) | R&N 2.1-4 |
| Mon, Feb 21 | President's Day | No Class | |
| Wed, Feb 23 | Uninformed Search | Lecture (Michael) | R&N 3.1-4 |
| Fri, Feb 25 | In-Class Activity: Search | Lecture (Michael) | |
| Mon, Feb 28 | A* and Adversarial Search | Lecture (Michael) | R&N 3.5-6, 5.1-3 |
| Wed, Mar 2 | Constraint Satisfaction Problems | Lecture (Michael) | R&N 6.1-5 |
| Fri, Mar 4 | In-Class Actibity: CSP | Lecture (Michael) | |
| Mon, Mar 7 | Spring Recess | No Class | |
| Wed, Mar 9 | Spring Recess | No Class | |
| Fri, Mar 11 | Spring Recess | No Class | |
| Mon, Mar 14 | AI Security Topics | Lecture (Michael) | |
| Wed, Mar 16 | Uncertainty in States | Lecture | R&N 12.1-4 |
| Fri, Mar 18 | Queries and Partial Observability | Lecture | R&N 13.1-2 We briefly discussed what a naive causal structure learning algorithm would look like. For a full treatment of constraint-based causal structure learning, see Shalizi Ch. 25 |
| Mon, Mar 21 | Causal Graphical Models | Lecture | Notebook exercises Actual notebook |
| Wed, Mar 23 | Modal Logics for Knowledge and Belief | Lecture | Modal Logic Playground DSL for belief programming with partial observability |
| Fri, Mar 25 | Representing agent knowledge with \(KT45^n\) | Lecture | An Introduction to Logics of Knowledge and Belief ICAPS 2020 Tutorial on Epistemic Planning |
| Mon, Mar 28 | Elementary Decision Theory | Lecture | Blog post |
| Wed, Mar 30 | Elementary Game Theory | Lecture | R&N 17.5, 17.6 Epistemic Game Theory Two-person Zero-sum Games Note that in this document, Player 1 chooses a row, whereas our Player 1 chooses a column |
| Fri, Apr 1 | Summary: Acting under incomplete or uncertain knowledge | Lecture | Probabilistic Modal Logic Factored Models for Probabilistic Modal Logic |
| SPIN Model Checking for the Verification of Clinical Guidelines | |||
| Mon, Apr 4 | Temporal Logic for Representing Transitions | Lecture | Last day to Withdraw R&N 14, 17 |
| Wed, Apr 6 | Exam Review | Review | |
| Fri, Apr 8 | Exam 3: Agent-based Reasoning | Exam | |
| Mon, Apr 11 | Exam 3: Agent-based Reasoning | Make-Up Exam | |
| Wed, Apr 13 | Probabilistic Modeling Review | In-class Work | Probabilistic Modeling Worksheet out |
| Fri, Apr 15 | Exam and programming assignment review | In-class Unit 3 review | Programming Assignment 2 out |
| Mon, Apr 18 | LTL Applications | Lecture | |
| Wed, Apr 20 | Markov Chains for Representing Transitions | Lecture | |
| Fri, Apr 22 | Markov Decision Processes | Lecture | Probabilistic Modeling worksheet due in class |
| Mon, Apr 25 | Learning Programs via Genetic Programming | Lecture | |
| Wed, Apr 27 | Learning Programs via Program Synthesis | Lecture | Peer-graded Probabilistic Modeling worksheet due in class |
| Fri, Apr 29 | Counter-example Guided Inductive Search | Lecture | Probabilistic Modeling worksheet solutions |
| Mon, May 2 | More Program Synthesis | Lecture | VerifAI: A Toolkit for the Formal Design and Analysis of Artificial Intelligence-Based Systems |
| Wed, May 4 | Exam Review | Review | |
| Fri, May 6 | Exam 4: Time and Programs | Exam | Last Day of Classes |
| Thu, May 12 | Final Exam | 7:30am-10:15, VOTEY 207 | All programming assignments due (hard) |