(Term 2, Session 201, 201011)
Overview  Grades  Text  Schedule  Assessing your own learning 
 Meeting Times: Monday, Wednesday, Friday, 3:00  4:00 PM
 First Class: Wednesday, January 5, 2011
 Location: DMP 110
 Instructor: Frank Hutter
 Instructor's Office Location: ISICS X560 (beta lab)
 Instructor's Office Hours: Monday, Wednesday, Friday, 44:30pm in X530 (i.e., immediately after each lecture); available other times by appointment.
 TA
Office Hours:
 Simona Radu: Monday, 10am11am (changed from 1112), X150 (Learning Center)
 Vasanth Rajendran: Thursday, 3pm4pm, X150 (Learning Center)
 Mike Chiang: Wed 1pm2pm, X150 (Learning Center)
 Course Discussion Board: (the place to submit your questions and get answers, as well as see answers given to others): log into WebCT Vista using your CWL
 AISpace: demo applets that illustrate some of the techniques covered in class
 Prerequisites: Either (a) CPSC 221 or (b) both of CPSC 216, CPSC 220 or (c) all of CPSC 211, CPSC 260, EECE 320.
 Final exam: TBA
What do I do if I get the flu?
 Selfisolate: stay away from campus until you're feverfree for 24 hours.
 Get a doctor's note if you're missing midterm or final, or if you'll be late for an assignment.
 Follow
the course on this page,
and contact
Frank if you have additional questions.
Grading Scheme: Evaluation will be based on a set of assignments, a midterm, and an exam. Important: you must pass the final in order to pass the course. The instructor reserves the right to adjust this grading scheme during the term, if necessary.
 Assignments  20%
 Midterm  30%
 Final  50%
If your grade improves substantially from the midterm to the final, defined as a final exam grade that is at least 20% higher than the midterm grade, then the following grade breakdown will be used instead.
 Assignments  20%
 Midterm  15%
 Final  65%
The assignment grade will be computed by adding up the number of points you get across all assignments, dividing this number by the number of possible points, and multiplying by 20. Assignments will not be graded out of the same number of points; this means that they will not be weighted equally.
Submitting assignments via handin: Assignments are to be handed in electronically via the handin tool. The WebCT discussion board for assignment 0 has instructions for how to do this. In order to use handin, you will need to activate your CS account (every registered student already has an account, you just need to activate it). You can activate your account on the acount activation page.
Late Assignments: Assignments are to be handed in electronically via Handin by 3pm on the due date (see the point above). However, every student is allotted four "late days", which allow assignments to be handed in late without penalty on four days or parts of days during the term. The purpose of late days is to allow students the flexibility to manage unexpected obstacles to coursework that arise during the course of the term, such as travel, moderate illness, conflicts with other courses, extracurricular obligations, job interviews, etc. Thus, additional late days will NOT be granted except under truly exceptional circumstances. If an assignment is submitted late and a student has used up all of her/his late days, 20% will be deducted for every day the assignment is late. (E.g., an assignment 2 days late and graded out of 100 points will be awarded a maximum of 60 points.)
How late does something have to be to use up a late day? A day is defined as a 24hour block of time beginning at 3 PM on the day an assignment is due. To use a late day, write the number of late days claimed on the first page of your assignment.. Examples:
 Handing in an assignment at the end of lecture on the day it is due consumes one late day.
 Handing in an assignment at 10:15 the morning after it is due consumes one late day.
 Handing in an assignment at 3:30 the day after it is due consumes two late days.
Missing Deadlines or Exams: In truly exceptional circumstances, when accompanied by a note from Student Health Services or a Department Advisor, the following arrangements will be made.
 If an assignment cannot be completed, the assignment grade will be computed based on the remaining assignments. Note that such an arrangement is extremely unusualthe late day system is intended to allow students to accommodate disruptions from moderate illness without contacting the instructor.
 If the midterm is missed, its grades will be shifted to the final. This means the final will count for 80% of the final grade, and assignments will count for the remaining 20%.
 If the final is missed, a makeup final will be scheduled. This makeup final will be held as soon as possible after the regularly scheduled final.
Academic Conduct: Submitting the work of another person as your own (i.e. plagiarism) constitutes academic misconduct, as does communication with others (either as donor or recipient) in ways other than those permitted for homework and exams. Such actions will not be tolerated. Specifically, for this course, the rules are as follows:
 For assignments 14 (not for assignment 0), you may work with one other student. That student must also be a CPSC 322 student this term, and you will both have to officially declare that you collaborated when submitting your assignment. Both of you will have to submit your assignments separately.
 You cannot work with or copy work from anyone else. You may not, under any circumstances, submit any solution not written by yourself, look at a student's solution who is not your official partner (this includes the solutions from assignments completed in the past), or previous sample solutions, and you may not share your own work with others. All work for this course is required to be new work and cannot be submitted as part of an assignment in another course without the approval of all instructors involved.
 You may, however, discuss your solutions and design decisions with your fellow students on a high level. In other words, you can talk about the assignments, but you cannot look at or copy other people's answers.
Violations of these rules constitute very serious academic misconduct, and they are subject to penalties ranging from a grade of zero on the current and *all* the previous assignments to indefinite suspension from the University. More information on procedures and penalties can be found in the Department's Policy on Plagiarism and collaboration and in UBC regulations on student discipline . If you are in any doubt about the interpretation of any of these rules, consult the instructor or a TA!
You can find the
course schedule and lecture slides
below. The schedule is tentative and will change throughout the term.
Future assignment due dates are
provided to give you a rough sense; however, they are also subject to
change. I will try to post the slides for each lecture by 2am the day
of the lecture; this allows you to print them when you get up in the
morning. I don't promise to use exactly that version in class, but it
should be very close. If I do further changes, I will post the final
version after class, at the latest when I post the slides for the next
lecture.
If you are curious what's coming up in future lectures, here are Cristina
Conati's slides from last term;
the content should be quite similar to this year but the form
differs
a lot (so these are NOT useful for note taking in class).
Date  Lecture 
Book Sections 
Notes 
(1) Wed, Jan 5  Intro 1: What is AI? [ppt] [pdf]  1.11.3 
Assignment 0 out 
(2) Fri, Jan 7  Intro 2: Representational Dimensions [ppt] [pdf]  1.41.5  
(3) Mon, Jan 10  Intro 3: Applications of AI [ppt] [pdf]  1.6 

(4) Wed, Jan 12  Search 1: Representation & Search Framework [ppt] [pdf]  3.03.4 
Assignment 0 due 
(5) Fri, Jan 14  Search 2: BFS and DFS [ppt] [pdf]  3.5  Exercise 1 , solutions 
(6) Mon, Jan 17  Search 3: Search with Costs & Heuristic Search [ppt] [pdf]  3.5.3,
3.6.1 

(7) Wed, Jan 19  Search 4: Heuristic Search: A* [ppt] [pdf]  3.6  Exercise 2
, solutions 
(8) Fri, Jan 21  Search 5: A* optimality, cycle checking [ppt] [pdf]  3.6 

(9) Mon, Jan 24  Search 6: Iterative Deepening (IDS) [draft:ppt pdf] [covered: ppt pdf]  3.7.3  Assignment 1 out 
(10) Wed, Jan 26  Search 7: Multiple Path Pruning, IDS [draft:ppt pdf] [covered: ppt pdf]  3.7.13.7.3  
(11) Fri, Jan 28  CSP 1: Branch & Bound. CSP: Intro [ppt] [pdf]  3.7 & 4.04.2  Exercise 3 , solutions 
(12) Mon, Jan 31  CSP 2: Solving CSP using search [ppt] [pdf]  4.34.4  
(13) Wed, Feb 2  CSP 3: Arc consistency [ppt] [pdf]  4.5 
Exercise
4 , solutions 
(14) Fri, Feb 4  CSP 4: Domain splitting [draft: ppt pdf] [covered: ppt pdf]  4.6  Sudoku programming question out 
(15) Mon,
Feb 7 
CSP
5: Local
search [ppt]
[pdf] 
4.8  Exercise
5 , solutions Assignment 1 due Assignment 2 out 
(16) Wed, Feb 9  CSP 6: Stochastic local search [ppt] [pdf]  4.8  
(17) Fri,
Feb 11 
CSP
7: Stochastic local
search algorithms [ppt]
[pdf] 
4.8  IBM's
Watson in the news: NYTimes,
wired
magazine More technical: AI magazine article: Building Watson Watch: video of practice round 
Mon, Feb 14  Reading break; university closed  
Wed, Feb 16  Reading break; university closed  
Fri, Feb 18  Reading break; university closed  
(18) Mon, Feb 21  Planning 1: Representation and Forward Planning [ppt] [pdf]  8.0, 8.1, 8,2  Exercise 6 , solutions 
(19) Wed, Feb 23  Planning 2: Forward Planning and CSP Planning [ppt] [pdf]  8.2,
8.4 
Assignment 2 due 
Fri, Feb 25  Midterm review (all whiteboard, no slides)  
Mon, Feb 28  Midterm (3pm4:30pm in FSC 1005)  Exercise 7 , solutions  
(20) Wed, Mar 2  Planning 3: CSP Planning wrap up. [ppt] [pdf]  8.4  Assignment 3 out 
(21) Fri, Mar 4  Logic 1: Intro & Propositional Definite Clause Logic [ppt] [pdf]  5.15.2 
Exercise 8 , solutions 
(22) Mon, Mar 7  Logic 2: Proof procedures, soundness and correctness [ppt] [pdf]  5.2  
(23) Wed, Mar 9  Logic 3: Bottomup Proof Procedure [ppt] [pdf]  5.2  Exercise 9 , solutions 
(24) Fri, Mar 11  Logic 4: TopDown Procedure and Datalog [ppt] [pdf]  5.2  
(25) Mon, Mar 14  Logic 5: wrapup [ppt] [pdf] & presentation: SLS for UBC exam scheduling  12.3  
(26) Wed,
Mar 16 
Uncertainty
1: Probability Theory [ppt]
[pdf] 
6.1, 6.1.1 
Exercise
10 , solutions Assignment 3 due 
(27) Fri, Mar 18  Uncertainty 2: Conditional Probability, Bayes Rule, Chain Rule [ppt] [pdf]  6.1.3  Assignment 4 out 
(28) Mon, Mar 21  Uncertainty 3: Independence [ppt] [pdf]  6.2  
(29) Wed, Mar 23  Uncertainty 4: Bayesian networks intro [ppt] [pdf]  6.3  6.3.1  
(30) Fri, Mar 25  Uncertainty 5: Independence and Inference [ppt] [pdf]  6.3.1  
(31) Mon, Mar 28  Uncertainty 6: Variable Elimination [ppt] [pdf]  6.4.1  Exercise 11 , solutions 
(32) Wed,
Mar 30 
Decision Theory 1: Uncertainty wrapup. Single Decisions [ppt] [pdf]  6.4.1 & 9.2  newspaper.xml 
(33) Fri, Apr 1  Decision Theory 2: Single and sequential decisions. VE. [ppt] [pdf]  9.29.3 
Exercise
12 , solutions bikeride_tires_flat_tools.xml 
(34) Mon, Apr 4  Decision Theory 3: optimal policies for sequential decisions [ppt] [pdf]  9.3  Assignment 4 due 
(35) Wed, Apr 6  Perspectives and Final Review [ppt] [pdf]  Exercise 13 , solutions wii.xml  
Mon, Apr 11  Final exam, 3:30 pm DMP 310. This is on the first day of exams. 
 We created a list of "learning goals" for the course, which detail concrete skills you should have after mastering each of the units. The list can be accessed via WebCT Vista.

Exercises are ungraded practice problems to help you prepare for assignments and exams. They're optional, but will definitely help you to master the course material. All exercises will be put up via WebCT Vista.
.