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CS 470/670 Artificial Intelligence
Tentative Course Schedule 
Fall 2011

Dr. Wei Ding

The course schedule is tentative. Actual contents to be covered depend on progress.
Lecture notes can be downloaded through your UMass Online Blackboard Learning System (BLS) account.

Section 01: Monday 05:30 PM - 06:45 PM, Wednesday 05:30 PM - 06:45 PM Class Number:3031/3032
Healey Library Low Level H-LL-3507
Weeks Meetings Topic Readings & Handouts Examples & Web Resources
1.  Sept. 5, no class, Labor Day (Holiday)
 Sept. 7  Course Administration    
2.  Sept. 12  Introduction to AI Ch 1
Slide: Introduction to AI
 Sept. 14  Solving Problem by Searching  Ch 3.1 ~ Ch 3.3
Slides: Solving Problem by Searching
3.  Sept. 19  Uninformed Search Strategies  Ch 3.4 ~ Ch 3.5
Slides: Uninformed Search Strategies
 Sept. 21  Presentation of Homework Assignment 1    
4.  Sept. 26  Uninformed Search Strategies (continued)
 Informed Search and Exploration
Ch 4
Slides: Informed Search and Exploration Part I
 Sept. 28  Informed Search and Exploration (continued)
5.  Oct. 3  Informed Search and Exploration (continued) Slides: Informed Search and Exploration Part II  
 Oct. 5  Informed Search and Exploration (continued)    
6.  Oct. 10, no class, Columbus Day (Holiday)
 Oct. 12  Informed Search and Exploration (continued) Slides: Informed Search and Exploration Part III  
7.  Oct. 17  Informed Search and Exploration (continued)    
 Oct. 19 Uncertainty Slides: Uncertainty  
8.  Oct. 24 Programming Assignment 2 Presentation  Ch 13 ~ Ch 14, Ch 20.2
Slides: Uncertainty
 Oct. 26  Kyle McGivney and Chrisopher Griffith: Discussion of Programming Assignment 1 (writing AI algorithms in OOD)
9.  Oct. 31  Uncertainty(continued)

Pre-Exam Review

 Nov. 2  Midterm Examination


The Official US Time
10.  Nov. 7  
Slides: Classification KNN  
 Nov. 9  Binh D. Tran: Leakage in Data Mining: Formulation, Detection, and Avoidance (KDD 2011)
11.  Nov. 14  Dawei Wang: How Good is Almost Perfect? (AAAI 2008)
Probabilistic Reasoning over Time  (continued)  
 Nov. 16   Pothan Chand Yarra: Optimal False-Name-Proof Voting Rules with Costly Voting (AAAI 2008)
Learning Probabilistic  Models
 Ch 20  
12.  Nov. 21  Mingbo Ma: Hilbert Space Embeddings of Hidden Markov Models (ICML 2010) Learning Probabilistic  Models  (continued)    
 Nov. 23  Kyle McGivney: A probabilistic framework for semi-supervised clustering (KDD 2004) Learning Probabilistic  Models   (continued)    
13.  Nov. 28  Classification: Basic Concepts    
 Nov. 30  Classification: Nearest-Neighbor Classifiers    
14.  Dec. 5  Classification: Decision Trees    
 Dec. 7  Classification: Ensemble Methods    
15.  Dec. 12  Final Exam    
 Dec. 14  Reading day (no class)    
16.  Dec. 19 (6:30PM -9:30PM))  Homework Presentation (Monday, 6:30-9:30 PM, Dec 19, 2011, Wheatley W01-0037)    


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