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 and Syllabus
| Date | Lecture | Misc |
|---|---|---|
| Problem Solving and Search | ||
| Thu Sep 02 | Introduction | |
| Tue Sep 07 | Search Problems | |
| Thu Sep 09 | Uninformed Search | |
| Tue Sep 14 | Informed Search | HW 1 Out |
| Thu Sep 16 | Constraint Satisfaction - I | |
| Tue Sep 21 | Constraint Satisfaction - II | |
| Thu Sep 23 | Adversarial Search - I | HW 1 Due, Proj 1 Out |
| Tue Sep 28 | Adversarial Search - II | |
| Optimization | ||
| Thu Sep 30 | Linear Programming | |
| Tue Oct 05 | Integer Programming | |
| Thu Oct 07 | Optimization | Proj 1 Due, HW 2 Out |
| Tue Oct 12 | Convex Optimization | |
| Sequential Decision Making | ||
| Thu Oct 14 | Markov Decision Processes - I | |
| Tue Oct 19 | Markov Decision Processes - II | HW 2 Due |
| Thu Oct 21 | Midterm Review | |
| Tue Oct 26 | No Class (Fall Break) | |
| Thu Oct 28 | Midterm Exam | |
| Tue Nov 02 | Reinforcement Learning - I | HW 3 Out |
| Thu Nov 04 | Reinforcement Learning - II | |
| Tue Nov 09 | Reinforcement Learning - III | |
| Probabilistic Reasoning | ||
| Thu Nov 11 | Introduction to Probability | HW 3 Due |
| Tue Nov 16 | Bayesian Networks: Representation | HW 4 Out |
| Thu Nov 18 | Bayesian Networks: Independence | |
| Tue Nov 23 | Bayesian Networks: Inference | Proj 2 Out |
| Thu Nov 25 | No Class - (Thanksgiving) | |
| Tue Nov 30 | Bayesian Networks: Sampling | |
| Thu Dec 02 | Hidden Markov Models | |
| Tue Dec 07 | Particle Filtering | HW 4 Due |
| Conclusion | ||
| Thu Dec 09 | AI Applications | Proj 2 Due |
| Wed Dec 15 | Final Exam (10:00am-12:00pm) |