CMPT 310: Artificial Intelligence Survey. Simon Fraser University, Burnaby Campus. Fall 2019.

Course logistics

• MoWeFr 11:30AM - 12:20PM in AQ3181.

Instructor: Maxwell Libbrecht

• Web page
• Office: TASC 1 9219
• Email: maxwl at sfu dot ca
• Office hours: Mondays 2:30-3:15pm.

TAs

• Ashkan Alinejad (aalineja at sfu dot ca)
• Heng Liu (liuhengl at sfu dot ca)
• Mehran Khodabandeh. (mkhodaba at sfu dot ca)

TA office hour times

• Office hour location: ASB 9814
• Every Tuesday 4:00 PM - 5:00 PM, Ashkan

• Additional Office hours for A3:
• Office hour location: ASB 9814
• Nov 08, 2019 (Fri) 09:00 am-10:00 am, Ashkan
• Nov 12, 2019 (Tue) 05:00 pm-06:00 pm, Ashkan
• Nov 13, 2019 (Wed) 04:00 pm-05:00 pm, Ashkan
• Nov 14, 2019 (Thu) 09:00 am-10:00 am , Ashkan
• Nov 15, 2019 (Fri) 09:00 am-10:00 am , Ashkan

• Office hours for A4:
• Office hour location: ASB 9808
• Nov 26, 2019 (Tue) 05:00 pm-06:00 pm, Mehran
• Nov 27, 2019 (Wed) 09:00 am-10:00 am, Mehran
• Nov 28, 2019 (Thu) 05:00 pm-06:30 pm, Mehran
• Nov 29, 2019 (Fri) 09:00 am-10:30 am, Mehran

Links

• Textbook website : Stuart Russell, Peter Norvig. "Artificial Intelligence: A Modern Approach"

• Piazza discussion forum

• Lecture slides

• Last year's web page

Coding assignments

• Assignment 0: Due Friday 2019-09-20 at 23:59.
• Assignment 1: Due Friday 2019-10-04 at 23:59.
• Assignment 2: Due Friday 2019-10-25 at 23:59.
• Assignment 3: Due Monday 2019-11-18 at 23:59.
• Assignment 4: Due Wednesday 2019-12-04 at 23:59.

Late days: You get five "late days". Turning in a coding assignment late costs one late day per day after the due date (rounded up). If you run out of late days, late submission costs 10% of the assignment grade per day. We may post solutions three days after the due date; no submissions will be accepted after 72 hours after the due date.

You are encouraged to work in a group. Feel free to discuss solution strategies and check each other’s work. However, you must write all the text and code you submit on your own. Joint submissions are not allowed, nor is copying someone else's text or code. Plagiarism is not okay, and will be taken very seriously. If you’re not sure whether something is okay, please ask on Piazza.

Exams

• Midterm: Will be held 2019-11-01 in class.
• Final exam: Monday 2019-12-09, 8:30-11:30am in RCBIMAGTH.

Grading breakdown

• 50% Coding assignments. 2% for Assignment 0, 12% for each other assignment.
• 15% Midterm.
• 35% Final.
• +5% Extra credit for discussion on Piazza. Max 50 points. 1 point = first answer to a simple question (logistics etc). 3 points = Accurate, in-depth response to a content question or in-depth discussion of AI-related topic. 3 points = First to point out nontrivial error in course materials (assignments, lecture slides, etc). 0 points = Asking a question to help yourself with assignments etc; "meta" discussion (logistics etc); response that is incorrect or unintelligible.

Expected grade breakpoints

Schedule and syllabus

• W36 9/2. No lecture Mon. Introduction.
• W37 9/9. Agents. Search; uninformed, informed. Python review.
• W38 9/16. Assignment 0 (Python) due Fri. Search.
• W39 9/23. Games; adversarial search.
• W40 9/30. Assignment 1 (Search) due Fri. Constraint satisfaction problems.
• W41 10/7. Logic, propositional logic.
• W42 10/14. No class Mon. Probability.
• W43 10/21. Assignment 2 (SAT) due Fri. Bayesian networks.
• W44 10/28. Midterm Fri. Midterm prep, Bayesian network inference.
• W45 11/4. No class Fri. Bayesian network inference.
• W46 11/11. Temporal Bayesian networks.
• W47 11/18. Assignment 3 (Viterbi) due Mon. Supervised machine learning, decision trees.
• W48 11/25. Neural networks.
• W49 12/2. Assignment 4 (Backpropagation) due Wed. Last lecture on Mon. Final prep.
• W50 12/9. No class Final Mon.