Spring 2023


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.

  1. The slides with audio work best on Safari, Chrome, Firefox and Opera. I have not tested other browsers.
  2. If you want to get a PDF of the slides, press ‘e’ and it should create a PDF that can be downloaded.
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  4. On Chrome you can select playback speed in the audio control bar at the bottom.
  5. Press ‘n’ to manually navigate forward, and ‘p’ to navigate backward. Do not use the left/right arrow keys.
  6. You can skip to any slide you like and simply press the play button at the bottom to listen to the audio for that slide.
  7. If the audio gets stuck or does not play, refresh that page. This is a known problem and occurs when the browser does not fully load the audio file.

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)