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

Course logistics

• D1: MoWeFr 9:30AM - 10:20AM in AQ3150.
• D2: MoWeFr 12:30PM - 1:20PM in AQ3005.

Instructor: Maxwell Libbrecht

• Web page
• Office: TASC 1 9219
• Email: maxwl at sfu dot ca
• Office hours: Mondays at 1:30pm

TAs:

• Heng Liu (liuhengl at sfu dot ca)
• Raquel Aoki (raoki at sfu dot ca)
• Mohammad Mazraeh (mmazraeh at sfu dot ca)

• Office hour location: ASB 9810
• Every Monday 11:30 AM - 12:30 PM, Mohammad
• Additional Office hours for coding assignment 1:
• Jan 18, 2019 (Fri) 02:30 PM - 03:30 PM Heng
• Jan 22, 2019 (Tue) 01:00 PM - 02:00 PM Heng
• Jan 23, 2019 (Wed) 03:30 PM - 04:30 PM Heng
• Jan 24, 2019 (Thu) 03:30 PM - 04:30 PM Heng
• Jan 25, 2019 (Fri) 03:00 PM - 04:00 PM Heng

• Additional Office hours for coding assignment 2:
• Feb 06, 2019 (Wed) 03:00 PM - 04:00 PM Heng
• Feb 08, 2019 (Fri) 03:00 PM - 04:00 PM Heng
• Feb 13, 2019 (Wed) 12:30 AM - 01:30 PM Raquel
• Feb 13, 2019 (Wed) 03:00 PM - 04:00 PM Raquel
• Feb 14, 2019 (Thu) 01:00 PM - 02:00 PM Raquel
• Feb 15, 2019 (Fri) 03:30 PM - 04:30 PM Heng

• Additional Office hours for coding assignment 3:
• Mar 12, 2019 (Tue) 01:30 PM - 02:30 PM Raquel
• Mar 13, 2019 (Wed) 03:00 PM - 04:00 PM Raquel
• Mar 14, 2019 (Thu) 01:00 PM - 02:00 PM Raquel
• Mar 19, 2019 (Tue) 10:00 AM - 11:00 AM Raquel
• Mar 20, 2019 (Wed) 04:00 PM - 05:00 PM Raquel
• Mar 22, 2019 (Fri) 03:00 PM - 04:00 PM Raquel

• Additional Office hours for coding assignment 4 (ASB9812):
• Apr 4, 2019 (Thu) 09:30 AM - 10:30 AM Mohammad
• Apr 5, 2019 (Fri) 09:30 AM - 10:30 AM Mohammad
• Apr 8, 2019 (Mon) 09:30 AM - 10:30 AM Mohammad
• Apr 9, 2019 (Mon) 09:30 AM - 10:30 AM Mohammad (ASB9814) [New]
• Apr 10, 2019 (Wed) 09:30 AM - 10:30 AM Mohammad
• Apr 11, 2019 (Thu) 09:30 AM - 10:30 AM Mohammad
• Apr 12, 2019 (Fri) 09:30 AM - 10:30 AM Mohammad

Links

• Piazza discussion forum

• Recorded lectures

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

• Solutions

Coding assignments

• Assignment 1: Due Friday 2019-01-25 at 23:59.
• Assignment 2: Due Friday 2019-02-15 at 23:59.
• Assignment 3: Due Friday 2019-03-22 at 23:59.
• Assignment 4: Due Friday 2019-04-12 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 will post solutions three days after the due date; no submissions will be accepted after that.

Exams

• Midterm: Will be held 2019-03-08 in class. (Makeup midterm: March 13, 11:00 am, ASB9820).
• Final exam: Friday 2019-04-24, 8:30-11:30am in RCB IMAGTH.

Schedule

• W1 12/31. No lecture Mon or Wed. Introduction (Ch1), Agents (Ch2).
• W2 1/7. Search; uninformed, informed (Ch3,4). Optional Python review.
• W3 1/14. Games; adversarial search (Ch6).
• W4 1/21. Assignment 1 due Fri. Constraint satisfaction problems (Ch5).
• W5 1/28. Logic, propositional logic (Ch7).
• W6 2/4. First-order logic (Ch8).
• W7 2/11. Assignment 2 due Fri. Probability (Ch13). Probability axioms 13.2.3, Bayes rule 13.5. Inference using joint distribution 13.3. Independence 13.4.
• W8 2/18. Reading break - no class.
• W9 2/25. Bayesian networks (Ch14). Inference by enumeration 14.4.1. Variable elimination 14.4.2. Sampling-based inference (rejection, Gibbs) 14.5.1-2.
• W10 3/4. Midterm Fri. Midterm prep, temporal models.
• W11 3/11. Temporal models (ch15). HMMs 15.3. Filtering and smoothing 15.2. Viterbi algorithm 15.2.3.
• W12 3/18. Assignment 3 due Fri. Supervised machine learning 18.2, decision trees 18.3.1-5. Evaluation 18.4.
• W13 3/25. Neural networks 18.7.
• W14 4/1. Neural networks.
• W15 4/8. Last lecture on Mon. Final prep. Assignment 4 due Fri.
• W16 4/15. No class
• W17 4/22. No class Final Wed.

Lecture slides

• chapter02
• chapter03
• chapter03b
• chapter05
• Uncertainty
• Bayesian networks

Written Assignments

• Written Assignment 01
• Written Assignment 02
• Written Assignment 03
• Written Assignment 04
• Written Assignment 05
• Written Assignment 06
• Written Assignment 07
• Written Assignment 08
• Written Assignment 09
• Written Assignment 10
• Written Assignment - Extra
• Written Assignment 11

Submission of extra questions for extra credit:

• Written assignment 01-05:
  • Please use the “Extra questions” Activity on CourSys. You can either submit one pdf file for each written assignment OR one pdf file with all extra questions.
  • If you submitted a Written Assignment with all 5 questions and you didn't use it to making up for the Friday absence:
    • Digital submissions: Please download and upload again the same file in "Extra questions";
    • Printed submissions: Please email the TA Raquel Aoki with your name, student number and assignments you want to be re-graded.

• Written Assignment 6 and later:
  • 5 questions BEFORE the deadline of the written assignment is due: Use the usual activity “Written Assignment”.
  • 5 questions AFTER the deadline of the written assignment is due: If you were able to submit only the 2 MANDATORY questions by the deadline of the written assignment using the “Written Assignment” activity, then submit the other 3 questions on the “Extra questions” activity.

If you have any further questions about it, please email the TA Raquel Aoki.

Grading breakdown

• [40%] Coding assignments.
• [5%] Participation in Friday group question solving.
• [10%] Written assignments.
• [15%] Midterm.
• [30%] Final.
• [+10%] Additional participation extra credit.

Expected grade breakpoints

Python Tutorial

• Sessions:
  • January 14th from 04:00 pm to 05:00 pm
  • January 16th from 11:00 am to 12:00 am
  • January 18th from 02:00 pm to 03:00 pm
• Room: ASB9804
• Duration: 1 hour
• File

• The same material will be covered in each session.
• Please register using this link and bring your own laptops with Jupyter Notebook installed (how to install link).