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.
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 ....TBD.....; 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.
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!
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 6  Course Overview [pdf]  
2 Fri, Sep 8  Inclass review test based on CPSC 322  Bonus: 1% of course grade 
3 Mon, Sep 11  Value of Info and Control  start Markov Decision Processes (MDPs) [pdf]  "322" Slides on Decision Networks and on Markov Chains 
4 Wed, Sep 13  MDP example start Value Iteration [pdf]  Practice Ex 9.C 
5 Fri, Sep 15  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 
6 Mon, Sep 18  Partially Observable MDPs (POMDPs) [pdf]  
7 Wed, Sep 20

POMDPs (cont') [pdf] 
 Assignment 1 out 
Blackjack.xml
See http://www.cs.uwaterloo. see also DESPOT JAIR 2017 
8 Fri, Sep 22 
Reinforcement Learning (RL) [pdf]

Practice Ex 11.A 
9 Mon, Sep 25  Reinforcement Learning (RL)(cont') [pdf]  
10 Wed, Sep 27  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

11 Fri, Sep 29  Finish RL  SARSA [pdf]  Ex 11.B 
12 Mon, Oct 2  Recap BNets  Start Approximate Inference in BNets [pdf]  Practice Ex 6.E, BN Company NorSys assignment1 due / assignment2 out hmw1.zip 
13 Wed, Oct 4  Approx. Inference  Likelihood Weighting, MCMC (Gibbs Sampling) [pdf]  BNet tool (with approx. inference algorithms) GeNIe 
14 Fri, Oct 6 
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) 
Mon, Oct 9  Thanksgiving  
15 Wed, Oct 11  Temporal Inference  HMM (Filtering, Prediction) [pdf]  
16 Fri, Oct 13  HMM (Smoothing, just start Viterbi) [pdf]  
17 Mon, Oct 16

Finish Viterbi  Approx. Inference in Temporal Models (Particle Filtering) [pdf]  
18 Wed, Oct 18  Intro Graphical Models Undirected Graphical Models  Markov Networks [pdf] 

19 Fri, Oct 20 
Inference in Markov Networks Conditional Random Fields (CRFs)
 Naive Markov [pdf]

assignment2 due FYI (not a required reading!) An Introduction to Conditional Random Fields. Charles Sutton, AndrewMcCallum. Foundations and Trends in Machine Learning 4 (4). 2012. 
20 Mon, Oct 23  Linear Chain CRFs  NLP applications[pdf]  MALLET 
Wed, Oct 25  Midterm exam (TBD) 
TBD 
21 Fri, Oct 27  Full Propositional Logics, Language and Inference [pdf]  
22 Mon, Oct 30  Finish Resolution, Satisfiability, WalkSAT [pdf]  
23 Wed, Nov 1  SAT encoding example  First Order Logics (FOL) [pdf]  assignmet3 out 
24 Fri, Nov 3 
Ontologies/Description Logics: Wordnet, UMLS, Yago, Probase..... [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 6  Similarity Measures: Concepts in Ontologies and Distributional for Words [pdf]  
26 Wed, Nov 8  NLP: ContextFree Grammars and Parsing [pdf] 
SKIP THIS PAPER THIS YEAR Paper Discussion (NLP) [ ppt ] [ pdf ] [DEMO]Carenini G., Ng R., Zwart E., Extracting Knowledge from Evaluative Text, Third International Conference on Knowledge Capture (KCAP 2005). Banff, Canada. October 25, 2005. [pdf] 
27 Fri, Nov 10  Probabilistic Context Free Grammar (1) [pdf]  
Mon, Nov 13  Remembrance Day  
28 Wed, Nov 15  Probabilistic Context Free Grammar (2) [pdf]   Berkeley Parser with demo 
29 Fri, Nov 17  Paper Discussion (NLP) on PCFG and CRFs [ppt] [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 DEMO 
30 Mon, Nov 20  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

31 Wed, Nov 22  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. 
32 Fri, Nov 24  Finish Markov Logics Inference + Applications [pdf]  (only if we cover plate notation) Practice Ex. 14.A 
33 Mon, Nov 27  Probabilistic Relational Models (1) Representation [pdf]  
34 Wed, Nov 29  Probabilistic Relational Models (2) Parameters and Inference [pdf]  
35 Fri, Dec 1  Beyond 3/422, AI research, Watson etc. [pdf] 
assignmet4 due  Some relevant papers form IUI15 Modeling users' interests 
TBD TBD  Final Exam Room: TBD  
