| Overview | Grades | Textbook | Schedule |
- Meeting Times: Monday, Wednesday, Friday, 1:00 - 2:00 PM
- First Class: Wed, Sept 5, 2012
- Location: DMP 110
- Instructor: Giuseppe Carenini
- Instructor's Office Location: CICSR 105
- Instructor's Office Hours: Tue 2-3 pm, my office.
- TA Office Hours:
- Nathaniel Tomer ntomer@cs.ubc.ca Fri 11am, X150 (Learning Center)
- Tatsuro Oya toya@cs.ubc.ca Wed 2 pm, X150 (Learning Center)
- Seyed Mehran Kazemi smkazemi@cs.ubc.ca Mon 11 am (NEW) , 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
Connect using your CWL
(Connect. substitutes Vista!).
If
you need assistance with Connect, there are number of resources available.
You can access those resources at
http://elearning.ubc.ca/
connect/student-resources/ If you who have technical issues with Connect should contact the IT Service Centre Help Desk at 604.822.2008 or http://it.ubc.ca/contact/helpdesk.html . - 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
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.
Late Assignments: Assignments are to be handed in BEFORE the start of lecture on the due date. However, every student is allotted four "late days", which allow assignments to be handed in late without penalty on three 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 24-hour block of time beginning at 1 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 2:30 the day after an assignment is due consumes two late days.
Assignments can be handed in electronically using handin; this is the only way to hand in late assignments over a weekend. Written work can also be put in Giuseppe's mailbox in the main CS office (room 201); ask the secretary to time-stamp it.
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 unusual--the 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 make-up final will be scheduled. This make-up 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:
- The written part of assignments is to be done alone. You may not, under any circumstances, submit any solution not written by yourself, look at another student's solution (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. In other words, you can talk about the assignments, but you cannot look at or copy other people's answers.
- The programming part of assignments is to be done either alone, or working with one other student. If you work with another student, each of you must hand in a copy of your work separately. You may not submit any solution not written by yourself and this one other student, look at other students' solutions (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.
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!
Further readings on topics covered in 322
Here is where you can find the course schedule and the PDF files from lectures. These dates will change throughout the term, but this schedule will be kept up to date. Assignment due dates are provided to give you a rough sense; however, they are also subject to change. I will try to always post the slides in advance (by 11am). After class, I will post the same slides inked with the notes I have added in class.
| Date | Lecture |
Book Chp |
Notes |
| Wed, Sep 5 | What is AI? [pdf ] | 1.1-1.3 | Assignment 0 |
| Fri, Sep 7 | Representational Dimensions [pdf ] | 1.4,1.5 | |
| Mon, Sep 10 | Applications of AI [pdf ] | 1.6 | Assignment 0 due |
| Wed, Sep 12 | Search: Intro [pdf ] | 3.1-3.4 | |
| Fri, Sep 14 | Search: Uninformed Search, DFS and BFS [pdf ] Tbots | 3.5.1, 3.5.2 | ex1 ex2 (AIspace) |
| Mon, Sep17 | Search: IDS, Search with Costs [ pdf ] | 3.7.3, 3.5.3 | ex3 |
| Wed, Sep 19 | Search: Heuristic Search [pdf ] | 3.6 intro | |
| Fri, Sep 21 | Search: BestFS, A*, optimality, [pdf ] | 3.6.1 | ex4 Assignment 1 out (see Connect) |
| Mon, Sep 24 | Search: Branch&Bound, IDA*, Pruning.... .... [ pdf ] | 3.7.1, 3.7.4 | |
| Wed, Sep 26 | Search: Dynamic Prog. and Recap [ pdf ] | ||
| Fri, Sep 28 | CSP Introduction [pdf ] | 4.1, 4.2 | |
| Mon, Oct 1 | CSPs: Search and Consistency [ pdf ] | 4.3, 4.4 | |
| Wed, Oct 3 | CSPs: Arc Consistency & Domain Splitting [ pdf ] | 4.5, 4.6 | |
| Fri, Oct 5 | CSPs: Local Search [ pdf ] | 4.8, 4.10 (intros) | Assignment 1 due |
| Mon, Oct 8 | Thanksgiving | ||
| Wed, Oct 10 | CSPs: Stochastic Local Search [ pdf ] | 4.8.1 - 4.8.3 | |
| Fri, Oct 12 | CSPs: SLS variants (Sim. Annealing + Pop. based) [ pdf ] | 4.9 | Assignment 2 out |
| Mon, Oct 15 | Planning: Representations and Forward Search [ pdf ] | 8.1 - 8.2 | Summary of Planning Competition 2008 (see slides 15-18 for participating planners, and slide 24 for domains) |
| Wed, Oct 17 | Planning: Heuristics and CSP Planning [ pdf ] | 8.4 | delivery robot STRIPS->CSP available in AI space simpleCommuting.xml complexCommuting.xml |
| Fri, Oct 19 | Logic: Intro and Syntax [ pdf ] | 5.1 - 5.1.1 - 5.2 | |
| Mon, Oct 22 | Logic: Semantics and Bottom-Up Proofs [ pdf ] | 5.1.2 - 5.2.2 | Assignment 2 due |
| Wed, Oct 24 | Logic: BU Sound and Complete [ pdf ] | 5.2.2.1 | |
| Fri, Oct 26 | Logic: Domain Modeling and Top-Down Proofs [ pdf ] | Ex. 5.5, 5.2.2.2 | |
| Mon, Oct 29 |
Midterm exam (50 mins, same room DMP 110 ) |
solution q.3 (load in AIspace) | |
| Wed, Oct 31 | Logic: Datalog [ pdf ] | 5.2, 12 (basic concepts) | ex5.9 exDatalog (load in AIspace) |
| Fri, Nov 2 | Uncertainty: Probability Theory [ pdf ] | 6.1, 6.1.1 | |
| Mon, Nov 5 | Uncertainty: Conditional Probability [ pdf ] | 6.1.3.1-2 | Assignment3 out |
| Wed, Nov 7 | Uncertainty: Conditional Independence [ pdf ] | 6.2 | |
| Fri, Nov 9 | Uncertainty: Belief Networks [ pdf ] | 6.3 | burglary example (load in AIspace) |
| Mon, Nov 12 | holiday in lieu of remembrance day | ||
| Wed, Nov 14 | Uncertainty: Belief Nets (indep. compactness, apps) [ pdf ] | 6.3-6.3.1 | email spam ex. (load in AIspace) |
| Fri, Nov 16 | Uncertainty: BNs inference ( intro Variable Elimination) [ pdf ] | ||
| Mon, Nov 19 | Uncertainty: Variable Elimination Example [ pdf ] | 6.4.1 | Assignment3 due |
| Wed, Nov 21 | Uncertainty: Temporal Probabilistic Models [ pdf ] | 6.5- 6.5.1 | Assignment4 out Blackjack.xml |
| Fri, Nov 23 | Uncertainty: Hidden Markov Models [ pdf ] | 6.5.2 | |
| Mon, Nov 26 | Decision Theory: Single-Stage Decisions [ pdf ] | 9.2 | robot example (load in AIspace) |
|
Wed, Nov 28 LAST CLASS |
Decision Theory: Sequential Decisions (policies) [ pdf ] | 9.3 | umbrella example (load in AIspace) Assignment4 due Fri Nov 30, 1PM. You can drop it at my office (ICICS 105)or by handin. |
| Decision Theory: VE , Value of Info and Control [pdf] | 9.4 | ||
| Decision Theory: MDPs [pdf] | |||
| Decision Theory: Finish MDPs [pdf] | |||
| Thu Dec13,3:30 pm |
Final Exam (3 hours, PHRM 1101) |