CSE 471: Introduction to Artificial Intelligence
Class: M/W, 1:30--2:45PM, PSH153
Office Hours: M/W, 3:00--4:00PM
TA: Akkamahadevi Hanni
| Course home | Syllabus | Schedule | Student Projects |
Subject to change.
Last updated: Aug 17, 2023
| Date | Topics | Lecture Notes |
Reading/Project Assignments | Deadlines | Important Dates |
| 1 | Course introduction. | Lecture Slides | Recommended: R&N, Third Edition, Chapter 1 | ||
| 2 | Rational agent. | Lecture Slides | Required: R&N, Third Edition, Chapter 2 | ||
| 3 | Search. | Lecture Slides | Required: R&N, Third Edition, Chapter 3.1--3.4 | ||
| 4 | Uninformed search. | Lecture Slides | Required: R&N, Third Edition, Chapter 3.1--3.4 | ||
| 5 | Informed search. | Lecture Slides | Required: R&N, Third Edition, Chapter 3.5--3.6 | ||
| 6 | Adversarial search. | Lecture Slides | Required: R&N, Third Edition, Chapter 5 | ||
| 7 | Adversarial search (cont) | Lecture Slides | Required: R&N, Third Edition, Chapter 5 | ||
| 8 | General games | Lecture Slides | Required: R&N, Third Edition, Chapter 5, 16 | ||
| 9 | Logic Agents | Lecture Slides | Required: R&N, Third Edition, Chapter 7 | ||
| 10 | Logic Agents (cont.) | Lecture Slides | Required: R&N, Third Edition, Chapter 7 | ||
| 11 | Logic Agents (cont.) | Lecture Slides | Required: R&N, Third Edition, Chapter 7 | ||
| 12 | First-order logic | Lecture Slides | Required: R&N, Third Edition, Chapter 9 | ||
| 13 | Markov Decision Process | Lecture Slides | Required: R&N, Third Edition, Chapter 17.1-2 | ||
| 14 | Markov Decision Process (cont.) | Lecture Slides | Required: RN, Third Edition, Chapter 17.1-2 | ||
| 15 | Markov Decision Process (cont.) | Lecture Slides | Required: RN, Third Edition, Chapter 17.1-2 | ||
| 16 | Spring break | ||||
| 17 | Spring break | ||||
| 18 | Reinforcement Learning | Lecture Slides | Required: RN, Third Edition, Chapter 21.1-3 | ||
| 19 | Reinforcement Learning (cont) | Lecture Slides | Required: RN, Third Edition, Chapter 21.4-5 | ||
| 20 | Reinforcement Learning (cont) | Lecture Slides | Required: RN, Third Edition, Chapter 21.4-5 | ||
| 21 | Probabilistic inference | Lecture Slides | Required: RN, Third Edition, Chapter 13 | ||
| 22 | Bayesian Network | Lecture Slides | Required: RN, Third Edition, Chapter 14 | ||
| 23 | Bayesian Network (cont) | Lecture Slides | Required: RN, Third Edition, Chapter 14 | ||
| 24 | Hidden Markov Model | Lecture Slides | Required: RN, Third Edition, Chapter 15 | ||
| 25 | Particle Filters | Lecture Slides | Required: RN, Third Edition, Chapter 15 | ||
| 26 | Decision Networks | Lecture Slides | Required: RN, Third Edition, Chapter 16 | ||
| 27 | Naive Bayes | Lecture Slides | Required: RN, Third Edition, Chapter 20 | ||
| 28 | Perceptron | Lecture Slides | Required: RN, Third Edition, Chapter 18 | ||
| 29 | Logistic Regression | Lecture Slides | Required: RN, Third Edition, Chapter 18 | ||
| 30 | Neural Networks | Lecture Slides | Required: RN, Third Edition, Chapter 18 | ||
| 31 | TBD | Lecture Slides |