CSE 471: Introduction to Artificial Intelligence
Class: ASU Sync, M/W, 4:30--5:45PM
Office Hours: ASU Sync, M/W, 3:00--4:00PM
Office Hours (TA: Akkamahadevi Hanni): ASU Sync, TBD
Office Hours (UGTA: Mitchell Bucklew): ASU Sync, TBD
| Course home | Syllabus | Schedule | Student Projects |
Subject to change. Check back frequently for updates.
Last updated: Jan 10, 2021
| Date | Topics | Lecture Notes |
Reading/Project Assignments | Deadlines | Important Dates |
| M. 01/11 | Course introduction. | Lecture Slides | Recommended: R&N, Third Edition, Chapter 1 | ||
| W. 01/13 | Rational agent. | Lecture Slides | Required: R&N, Third Edition, Chapter 2 | ||
| M. 01/18 | Holiday observed; class excused | Lecture Slides | |||
| W. 01/20 | Search. | Lecture Slides | Required: R&N, Third Edition, Chapter 3.1--3.4 | ||
| M. 01/25 | Uninformed search. | Lecture Slides | Required: R&N, Third Edition, Chapter 3.1--3.4 | ||
| W. 01/27 | Informed search. | Lecture Slides | Required: R&N, Third Edition, Chapter 3.5--3.6 | ||
| M. 02/01 | Adversarial search. | Lecture Slides | Required: R&N, Third Edition, Chapter 5 | ||
| W. 02/03 | Adversarial search (cont) | Lecture Slides | Required: R&N, Third Edition, Chapter 5 | ||
| M. 02/08 | General games | Lecture Slides | Required: R&N, Third Edition, Chapter 5, 16 | ||
| W. 02/10 | Logic Agents | Lecture Slides | Required: R&N, Third Edition, Chapter 7 | ||
| M. 02/15 | Logic Agents (cont.) | Lecture Slides | Required: R&N, Third Edition, Chapter 7 | ||
| W. 02/17 | Logic Agents (cont.) | Lecture Slides | Required: R&N, Third Edition, Chapter 7 | ||
| M. 02/22 | First-order logic | Lecture Slides | Required: R&N, Third Edition, Chapter 9 | ||
| W. 02/24 | Markov Decision Process | Lecture Slides | Required: R&N, Third Edition, Chapter 17.1-2 | ||
| M. 03/01 | Markov Decision Process (cont.) | Lecture Slides | Required: RN, Third Edition, Chapter 17.1-2 | ||
| W. 03/03 | Markov Decision Process (cont.) | Lecture Slides | Required: RN, Third Edition, Chapter 17.1-2 | ||
| M. 03/08 | Reinforcement Learning | Lecture Slides | Required: RN, Third Edition, Chapter 21.1-3 | ||
| W. 03/10 | Reinforcement Learning (cont) | Lecture Slides | Required: RN, Third Edition, Chapter 21.4-5 | ||
| M. 03/15 | Reinforcement Learning (cont) | Lecture Slides | Required: RN, Third Edition, Chapter 21.4-5 | ||
| W. 03/17 | Probabilistic inference | Lecture Slides | Required: RN, Third Edition, Chapter 13 | ||
| M. 03/22 | Bayesian Network | Lecture Slides | Required: RN, Third Edition, Chapter 14 | ||
| W. 03/24 | Bayesian Network (cont) | Lecture Slides | Required: RN, Third Edition, Chapter 14 | ||
| M. 03/29 | Hidden Markov Model | Lecture Slides | Required: RN, Third Edition, Chapter 15 | ||
| W. 03/31 | Particle Filters | Lecture Slides | Required: RN, Third Edition, Chapter 15 | ||
| M. 04/05 | Decision Networks | Lecture Slides | Required: RN, Third Edition, Chapter 16 | ||
| W. 04/07 | Naive Bayes | Lecture Slides | Required: RN, Third Edition, Chapter 20 | ||
| M. 04/12 | Perceptron | Lecture Slides | Required: RN, Third Edition, Chapter 18 | ||
| W. 04/14 | Logistic Regression | Lecture Slides | Required: RN, Third Edition, Chapter 18 | ||
| M. 04/19 | Neural Networks | Lecture Slides | Required: RN, Third Edition, Chapter 18 | ||
| W. 04/21 | TBD | Lecture Slides |