Overview  Grades  Textbook  Schedule  Readings 
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.
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  15%
Readings: Questions and Summaries  10%
Midterm  15%
Final  60%
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.
Your overall 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 15. Assignments will not be graded out of the same number of points; this means that they will not be weighted equally.
Working with a partner on assignments is highly recommended. In order to promote this kind of collaboration, you will receive a 5% bonus to any assignment where you work with a partner. for example, if an assignment is out of 100 points, you will receive 5 bonus points on it if you work with a partner (for a maximum of 105/100, which can "spill over" onto other assignments but cannot be used to bring your overall assignment grade over 100%). Note: to optimize your learning, you should actively collaborate with your partner, rather than simply having each partner work on part of the assignment. Your partnership should only submit one copy of the assignment; if both members submit, then you will not receive the partnership bonus.Assignments are to be typed (not handwritten) and submitted electronically on Canvas before the start of lecture on the due date. For each assignment, your submission must be formatted as a single PDF file. That means:
No Word files (all modern word processors have the option to save as PDF)
No zip/tar/rar/etc. files
No submissions with multiple files
Submissions failing to meet these formatting requirements will not be graded.
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 24hour block of time beginning at 1PM 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:
Assignments can be handed in electronically using ....Canvas.....; 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 timestamp 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.
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.
You may ask questions about assignment questions on discussion boards. However, you may not publicly post your work or solutions (whether complete or partial).
You may fully collaborate with your partner. You may also discuss the assignments with other students; however:
You may not show your work to other students or look at other students' work (the same applies to sharing answers or checking whether you got the same answers as other students)
You may not take away any written record of your discussions with other students
After discussions with other students, you must wait at least half an hour before working on the assignment, to help ensure that you are working from your own understanding of the material.
On the first page of each assignment submission, you must acknowledge any students outside your partnership with whom you discussed the assignment.
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 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. If you are in any doubt about the interpretation of any of these rules, consult the instructor or a TA!
For information on department policies related to student wellbeing,
please visit
https://www.cs.ubc.ca/students/undergrad/resources/equityinclusionwellness.
Readings: what to do (Please check if email mentioned in the link is still valid)
Here is where you can find the course schedule and the PPT and 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 12PM). After class, I will post the same slides inked with the notes I have added in class.
Date  Lecture  Notes 
1 Wed, Sep 4  Course Overview [pdf]  
2 Fri, Sep 6  Value of Info and Control  start Markov Decision Processes (MDPs) [pdf]  "322" Slides on Decision Networks and on Markov Chains 
3 Mon, Sep 9  MDP example start Value Iteration [pdf]  Practice Ex 9.C 
4 Wed, Sep 11  finish MDPs Value Iteration [pdf] 
FYI (not a required reading!) Planning with Markov Decision Processes: An AI Perspective, Synthesis Lectures on Artificial Intelligence and Machine Learning June 2012, 210 pages 
5 Fri, Sep 13  Partially Observable MDPs (POMDPs) [pdf]  
6 Mon, Sep 16  POMDPs (cont') [pdf] 
 Assignment 1 out 
Blackjack.xml
See http://www.cs.uwaterloo. see also DESPOT JAIR 2017 
7 Wed, Sep 18

Reinforcement Learning (RL) [pdf]

Practice Ex 11.A 
8 Fri, Sep 20  Reinforcement Learning (RL)(cont') [pdf]  
9 Mon, Sep 23  Paper Discussion MDP for scheduling (Medicine) [ppt] [pdf] YOUR QUESTIONS 
A Markov decision process approach to
multicategory patient scheduling in a diagnostic facility,
Artificial Intelligence in Medicine Journal, 2011
[pdf]
MDPs vs. Heuristic Methods

10 Wed, Sep 25  Finish RL  SARSA [pdf]  Ex 11.B 
11 Fri, Sep 27  Recap BNets  Start Approximate Inference in BNets [pdf]  Practice Ex 6.E, BN Company NorSys assignment1 due / assignment2 out hmw1.zip 
12 Mon, Sep 30  Approx. Inference  Likelihood Weighting, MCMC (Gibbs Sampling) [pdf]  BNet tool (with approx. inference algorithms) GeNIe 
13 Wed, Oct 2 
Paper Discussion
(ITS) 
Paper on
application of a relatively large BNet (where approx. inference is needed) slides [pdf] YOUR QUESTIONS 
Using Bayesian Networks to Manage Uncertainty in Student Modeling. Journal of User Modeling and UserAdapted Interaction 2002 [pdf] Dynamic BN (required only up to page 400) 
14 Fri, Oct 4  Temporal Inference  HMM (Filtering, Prediction) [pdf]  
15 Mon, Oct 7  HMM (Smoothing, just start Viterbi) [pdf]  
16 Wed, Oct 9  Finish Viterbi  Approx. Inference in Temporal Models (Particle Filtering) [pdf]  
17 Fri, Oct 11  Intro Graphical Models Undirected Graphical Models  Markov Networks [pdf]  
Mon, Oct 14  Thanksgiving  
18 Wed, Oct 16

Inference in Markov Networks Conditional Random Fields (CRFs)
 Naive Markov [pdf]

FYI (not a required reading!) An Introduction to Conditional Random Fields. Charles Sutton, AndrewMcCallum. Foundations and Trends in Machine Learning 4 (4). 2012. 
19 Fri, Oct 18  Linear Chain CRFs  NLP applications [pdf]  MALLET assignment2 due 
20 Mon, Oct 21  Full Propositional Logics, Language and Inference [pdf]  
21 Wed, Oct 23  Finish Resolution, Satisfiability, WalkSAT [pdf] 

Fri, Oct 25  Midterm exam (same time / room as regular class) Will start at 4 sharp  
22 Mon, Oct 28  SAT encoding example  First Order Logics (FOL) [pdf]  
23 Wed, Oct 30  Ontologies/Description Logics: Wordnet, UMLS, Yago, Probase..... [pdf]  assignmet3 out 
24 Fri, Nov 1 
Similarity Measures: Concepts in Ontologies and Distributional for Words [pdf] 
 Wordnet
and YAGO
(Wikipedia + Wordnet + GeoNames).
See also MS Research Probase, Google Knowledge Graph and Freebase and MS Concept Graph  (Domain
specific thesaurus)
Medical Subject
Headings (MeSH) 
25 Mon, Nov 4  NLP: ContextFree Grammars and Parsing [pdf] 
(for next three lectures) Russell and Norvig's Artificial Intelligence: A Modern Approach (third edition) [webpage] Part VII Communicating, Perceiving, and
Acting 
26 Wed, Nov 6  Probabilistic Context Free Grammar (1) [pdf]  
XX Fri, Nov 8  Cancelled  
Mon, Nov 11  Remembrance Day  
27 Wed, Nov 13  Probabilistic Context Free Grammar (2) [pdf]   Berkeley Parser with demo 
28 Fri, Nov 15  Paper Discussion (NLP) on PCFG and CRFs [ppt] [pdf] studentquestions [pdf] 
portions of CL paper
CODRA: A Novel Discriminative Framework for Rhetorical Analysis. Computational Linguistics (CL (2015)) Vol. 41, No. 3: 385–435, MIT press only sections 1, 3 and 4 are mandatory CODE  DEMO 
29 Mon, Nov 18  Markov Logics (1) Representation [pdf] 
assignmet3 due  assignmet4 out Markov Logic: An Interface Layer for Artificial Intelligence
P. Domingos University of Washington
D. Lowd
University of Oregon
2009 
30 Wed, Nov 20  Markov Logics (2) Inference [pdf]  Alchemy is a software package providing a series of algorithms for statistical relational learning and probabilistic logic inference, based on the Markov logic representation. 
31 Fri, Nov 22  Finish Markov Logics Inference + Applications [pdf]  (only if we cover plate notation) Practice Ex. 14.A 
32 Mon, Nov 25  Probabilistic Relational Models (1) Representation [pdf] 
Sample application to recommender systems 
33 Wed, Nov 27  Probabilistic Relational Models (2) Parameters and Inference [pdf]  
34 Fri, Nov 29  Beyond 3/422, AI research, Watson etc. [pdf] 
assignmet4 due  Some relevant papers form IUI15 Modeling users' interests 
Sat, Dec 7  Final Exam 15:30 PM 2.5 hours Room:CHBE 101  
