- Meeting Times: Tuesday, Thursday, 1:00 - 4:30 PM
- First Class: Tue, May 16, 2017
- Location: DMP 310
- Instructor: Giuseppe Carenini
- Instructor's Office Location: CICSR 105
- Instructor's Office Hours: Fri 830-930, my office CICSR 105 .
- TA Office Hours:
•Johnson, David email@example.com Office hour: ICCS X141, Wed 1-230pm•Johnson, Jordon firstname.lastname@example.org Office hour: ICCS X141, Mon 11-1pm•Kazemi, S. Mehran email@example.com Office hour: ICCS X141, Wed 230-4pm•Rahman, MD Abed firstname.lastname@example.org Office hour: ICCS X141, Fri 3-430pm
- Wang, Wenyi email@example.com Office hour: ICCS X141, Mon 1-230pm
- Course Discussion Board: (the place to submit your questions and get answers, as well as see answers given to others) Sign up: https://piazza.com/ubc.ca/summer2017/cpsc322
- 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
- 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. Specific rules for this course will be clearly described on each assignment.
Violations of the 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!
Complete book available online
Although this text will be our main reference for the
class, it must be stressed that you will need to know all the material
covered in class, whether or not it is included in the readings or
available on-line. Likewise, you are responsible for all the
material in assigned readings, whether or not it is covered in class.
If you'd like to refer to an alternate text, I recommend
Russell and Norvig's Artificial Intelligence: A Modern Approach (third edition).
I've arranged for a copy to be put on reserve in the
CS reading room.
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.
|Tue, May 16||What is AI? [pdf ]||1.1-1.3|
|Representational Dimensions [pdf ]||1.4,1.5|
|Applications of AI [pdf ]||1.6|
|Thur, May 18||Search: Intro [pdf ]||3.1-3.4|
|Search: Uninformed Search, DFS and BFS [pdf ]||3.5.1, 3.5.2||ex1 ex2 (AIspace)|
|Search: IDS, Search with Costs [ pdf ]||3.7.3, 3.5.3||ex3|
|Tue, May 23||Search: Heuristic Search [pdf ]||3.6 intro||Assignment 1 out (see Connect)|
|Search: BestFS, A*, optimality, [pdf ]||3.6.1||ex4|
|Search: Branch&Bound, IDA*, Pruning.... .... [ pdf ]||3.7.1, 3.7.4|
|Thur, May 25||Search: Dynamic Prog. and Recap [ pdf ]|
|CSP Introduction [pdf ]||4.1, 4.2|
|CSPs: Search and Consistency [ pdf ]||4.3, 4.4|
|Tue, May 30||CSPs: Arc Consistency & Domain Splitting [ pdf ]||4.5, 4.6||Assignment 1 due|
|CSPs: Local Search [ pdf ]||4.8, 4.10 (intros)|
|CSPs: Stochastic Local Search [ pdf ]||4.8.1 - 4.8.3|
|Thur, Jun 1||CSPs: SLS variants (Sim. Annealing + Pop. based) [ pdf ]||4.9||Assignment 2 out (see Connect)|
|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)|
|Planning: Heuristics and CSP Planning [ pdf ]||8.4||delivery robot STRIPS->CSP available in AI space simpleCommuting.xml complexCommuting.xml|
|Tue, Jun 6||Logic: Intro and Syntax [ pdf ]||5.1 - 5.1.1 - 5.2|
|Logic: Semantics and Bottom-Up Proofs [ pdf ]||5.1.2 - 5.2.2|
|Logic: BU Sound and Complete [ pdf ]||184.108.40.206||solution q.3 (load in AIspace)|
|Thur, Jun 8||Midterm exam (50 mins, ?same room DMP 310 ?)||Assignment 2 due|
|Logic: Domain Modeling and Top-Down Proofs [ pdf ]||Ex. 5.5, 220.127.116.11|
|Logic: Datalog [ pdf ]||5.2, 12 (basic concepts)||ex5.9 exDatalog (load in AIspace)|
|Tue, Jun 13||Uncertainty: Probability Theory [ pdf ]||6.1, 6.1.1||Assignment3 out (see Connect)|
|Uncertainty: Conditional Probability [ pdf ]||18.104.22.168-2|
|Uncertainty: Conditional Independence [ pdf ]||6.2|
|Thur, Jun 15||Uncertainty: Belief Networks [ pdf ]||6.3||burglary example (load in AIspace)|
|Uncertainty: Belief Nets (indep. compactness, apps) [ pdf ]||6.3-6.3.1||email spam ex. (load in AIspace)|
|Uncertainty: BNs inference ( intro Variable Elimination) [ pdf ]|
|Tue, Jun 20||Uncertainty: Variable Elimination Example [ pdf ]||6.4.1||
Assignment4 out Blackjack.xml (see Connect)
|Uncertainty: Temporal Probabilistic Models [ pdf ]||6.5- 6.5.1|
|Uncertainty: Hidden Markov Models [ pdf ]||6.5.2|
|Thur, Jun 22||Decision Theory: Single-Stage Decisions [ pdf ]||9.2||robot example (load in AIspace)|
|Decision Theory: Sequential Decisions (policies) [ pdf ]||9.3||umbrella example (load in AIspace) Assignment4 due Mon Dec 2, 1PM. You can drop it at my office (ICICS 105)or by handin.|
|Decision Theory: MDPs [pdf]||9.4|
|Decision Theory: Finish MDPs [pdf]|
|Decision Theory: VE , Value of Info and Control [pdf]||Assignment4 due|
Final Exam (2.5 hours, ) Room: