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 GitHub Classroom for all the assignments in this course. Sign up instructions will be sent to your email.
Course Policy
Details on course policies can be found here.
Navigating the Lecture Slides
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Schedule and Syllabus
Date | Lecture | Misc |
---|---|---|
Problem Solving and Search | ||
Tue Jan 10 | Introduction | |
Thu Jan 12 | Search Problems | |
Tue Jan 17 | Uninformed Search | Proj 1 Out |
Thu Jan 19 | Informed Search | |
Tue Jan 24 | Constraint Satisfaction - I | |
Thu Jan 26 | Constraint Satisfaction - II | |
Tue Jan 31 | Adversarial Search - I | |
Thu Feb 02 | Adversarial Search - II | |
Optimization | ||
Tue Feb 07 | Linear Programming | |
Thu Feb 09 | Integer Programming | |
Tue Feb 14 | No Class | |
Thu Feb 16 | No Class | |
Tue Feb 21 | Optimization | Proj 1 Due, Proj 2 Out |
Thu Feb 23 | Convex Optimization | |
Tue Feb 28 | Midterm Review | |
Thu Mar 02 | Midterm Exam | |
Tue Mar 07 | No Class (Spring Break) | |
Thu Mar 09 | No Class (Spring Break) | |
Tue Mar 14 | Midterm Exam | |
Sequential Decision Making | ||
Thu Mar 16 | Markov Decision Processes - I | Proj 2 Due, Proj 3 Out |
Tue Mar 21 | Markov Decision Processes - II | |
Thu Mar 23 | Reinforcement Learning - I | |
Tue Mar 28 | Reinforcement Learning - II | |
Thu Mar 30 | Reinforcement Learning - III | |
Probabilistic Reasoning (Probability Background) | ||
Tue Apr 04 | Bayesian Networks: Representation | |
Thu Apr 06 | Bayesian Networks: Independence | Proj 3 Due, Proj 4 Out |
Tue Apr 11 | Bayesian Networks: Inference | |
Thu Apr 13 | Bayesian Networks: Sampling | |
Tue Apr 18 | Hidden Markov Models | |
Thu Apr 20 | Particle Filtering | |
Conclusion | ||
Tue Apr 25 | ChatGPT and AI Applications | |
Thu Apr 27 | Course Review | Proj 4 Due |
Fri May 05 | Final Exam (07:45am-09:45am) |