CPSC 502 - Winter 2011 - Artificial Intelligence I

Readings, Grading, Syllabus, Assignments, Software&Data


Readings
Required
Reference

(Tentative) Grading Scheme
5% Class Participation
25% Assignments
30% Midterm exam
10% Your Presentation
30% Your Essay


Syllabus, Assignments, Software& Data

1
Sep 8 Th  Intro and Course Overview [pdf]    Assignment0

(due Sep 13Tu, in class)

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2
Sep 13Tu Search [pdf] Do all the Graph Searching exercises  Please look at the solutions only after you have tried hard to solve the problems AIspace:  demo applets that illustrate some of the techniques covered in class
3
Sep 15Th Constraint Satisfaction Problems (CSP) [pdf] Work on exercises 4.A and 4.B  
4
Sep 20Tu  (Stochastic) Local Search (SLS) [pdf] Work on exercise 4.C

- assign1 Search,  CSP, Planning, Logics, due Oct 4Tu

scheduler.zip
5
Sep 22Th Planning [pdf]   reference book on Planning (new edition will come out soon)

Planning competitions

6
Sep 27Tu Logics   [pdf]    
7
Sep 29Th cancelled    
 8
Oct 4Tu  Finish Logics - Belief Networks (BN) [pdf] a1due ProbInfoTheory Handout
 9
Oct 6Th Belief Networks (BN) [pdf] assign2 BN DN,  due Oct 20Th  
 10 Oct 11Tu
BN Approx Inference- Start MC and HMM [pdf]    
11
Oct 13Th
Hidden Markov Models (HMM) [pdf]    
12
Oct 18Tu Decision Networks [pdf]    
13
Oct 20Th MDP [pdf] a2due  
14
Oct 25Tu POMDP [pdf] assign3-Part1  HMM, MDP  Nov 3 Thu?  
15
Oct 27Th Supervised Machine Learning [pdf]   WEKA Data Mining Software in Java (includes Random Forests)

BoosTexter

16 Nov 1 Tu Supervised Machine Learning [pdf]   LIBLINEAR -- A Library for Large Linear Classification
17
Nov 3Th  Unsupervised Machine Learning [pdf] a3-Part1 due  
18
Nov  8Tu Reinforcement Learning [pdf] assign3-Part2 assign3-part2-data
19
Nov 10Th no class    
  Nov 14 Mon MIDTERM EXAM ( @5:30-7pm  Room: DMP 201   )
    Readings (AI models/techniques used)

20
Nov 17Th AI <=> NLP
  1. Summarizing Spoken and Written Conversations EMNLP 2008 [pdf]  Machine Learning - Classification - Logistic Regression  Emily - Debanga Ray
  2. Multi-document Biography Summarization EMNLP 2004 [pdf] Comparison of different Classifiers including NB and DTs    Sara  
21
Nov 22Tu AI <=> HCI
  1. Analysis of Adjective-Noun Word Pair Extraction Methods for Online Review Summarization (ijcai 2011) [pdf] (original paper won Best Paper Award at CHI-11). Great application of NLP (PartOfSpeech tagging) to HCI. The POS tagger they used is quite advanced. However POS can be done with HMMs. I will distribute an short handout on this, to be read with the paper.   Mahsa
  2. Human-Guided Machine Learning for Fast and Accurate Network Alarm Triage (ijcai 2011) [pdf] (original paper won Best Paper Award at CHI-11).  Interactive Machine Learning  Ben 
22
Nov 24Th AI in Medicine              a3-Part2 due
  1. A Graphical Decision-Theoretic Model for Neonatal Jaundice, Medical Decision Making Journal, 2007 [pdf] Decision Networks, Multi-Attribute Utility Functions    Mareija - Albert
  2. A Markov decision process approach to multi-category patient scheduling in a diagnostic facility, Artificial Intelligence in Medicine Journal, 2011 [pdf] MDPs vs. Heuristic Methods  Matt  
23 Nov 29Tu CSP in scheduling and bioinformatics
  1. Optimizing Limousine Service with AI, IAAI, 2010, selected for presentation in AI magazine Summer 2011[pdf] CSP, Scheduling  Kai - John
  2. A New Algorithm for RNA Secondary Structure Design, Journal of Molecular Biology 2004 [pdf] Stochastic Local Search Shu 
24 Dec 1Th AI in Education
  1. Using Bayesian Networks to Manage Uncertainty in Student Modeling. Journal of User Modeling and User-Adapted Interaction 2002  [pdf]  Dynamic BN  (required only up to page 400) Lingxia 
  2. Eye-Tracking to Model and Adapt to User Meta-cognition in Intelligent Learning Environments. Proceedings of  International Conference on Intelligent User Interfaces, 2006 [pdf] Dynamic BN and NB classifier   Sarwar
25 Dec 6 Tue AI in Software Engineering  and Graphics
  1. Classifying Software Changes: Clean or Buggy?,  IEEE Transactions on Software Engineering 2008 [pdf] Machine Learning - Classification - SVM: a technique we have not seen in class  , Shailendra
  2. Removing camera shake from a single image, SIGGRAPH, 2006 [pdf]  Advanced Bayesian Learning    Felix

 




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