Course Description
Fundamental issues in intelligent systems. Knowledge representation and mechanisms of reasoning. Search and constraint satisfaction. Agents. Application areas of AI and current topics. Students will learn basic concepts of artificial intelligence and get some hands on experience to solve real-life problems. Python will be used for all the assignments.
Textbook
B1. Artificial Intelligence: A Modern Approach
B2. Convex Optimization (optional)
B3. Applied Mathematical Programming (optional)
Communication
All written communication should be directed though Piazza. Sign up instructions will be sent to your email. You can post publicly or privately depending on your preference. Emails won’t be responded to. Except for the sign-up phase of the class, we will not be using be D2L for anything else.
Assignments
We will be using Mimir Classroom for all the assignments in this course. Sign up instructions will be sent to your email.
Schedule
| Date | Lecture | Misc |
|---|---|---|
| Problem Solving and Search | ||
| Thu Sep 03 | Introduction | |
| Tue Sep 08 | Search Problems | |
| Thu Sep 10 | Uninformed Search | |
| Tue Sep 15 | Informed Search | Proj 1 Out |
| Thu Sep 17 | Constraint Satisfaction - I | |
| Tue Sep 22 | Constraint Satisfaction - II | |
| Thu Sep 24 | Adversarial Search - I | |
| Tue Sep 29 | Adversarial Search - II | Proj 1 Due, HW 1 Out |
| Optimization | ||
| Thu Oct 01 | Linear Programming | |
| Tue Oct 06 | Integer Programming | HW 1 Due |
| Thu Oct 08 | Optimization | HW 2 Out |
| Tue Oct 13 | Convex Optimization | |
| Sequential Decision Making | ||
| Thu Oct 15 | Markov Decision Processes - I | Proj 2 Out |
| Tue Oct 20 | Markov Decision Processes - II | |
| Thu Oct 22 | Midterm Exam | Online |
| Tue Oct 27 | Reinforcement Learning - I | |
| Thu Oct 29 | Reinforcement Learning - II | Proj 2 Due, HW 3 Out |
| Tue Nov 03 | No Class - (Election Day) | |
| Thu Nov 05 | Mid-Term Exam Discussion | |
| Probabilistic Reasoning | ||
| Tue Nov 10 | Reinforcement Learning - III | HW 3 Due, Proj 3 Out |
| Thu Nov 12 | Introduction to Probability | |
| Tue Nov 17 | Bayesian Networks: Representation | |
| Thu Nov 19 | Bayesian Networks: Independence | |
| Tue Nov 24 | Bayesian Networks: Inference | Proj 3 Due, Proj 4 Out |
| Thu Nov 26 | No Class - (Thanksgiving) | |
| Tue Dec 01 | Bayesian Networks: Sampling | |
| Thu Dec 03 | Hidden Markov Models | HW 4 Out |
| Tue Dec 08 | Particle Filtering | HW 4 Due |
| Conclusion | ||
| Thu Dec 10 | AI Applications | Proj 4 Due |
| Wed Dec 16 | Final Exam (10:00am-12:00pm) | Online |