Local-copy-cs445

From GersteinInfo

This page contains general information for the class:

CPSC445/CPSC545/MBB334/MBB545/CBB545 Spring 2008

Introduction to Data Mining

Contents 1 Course websites 2 Homework 3 Final Project 4 Slides 4.1 Week 1 4.2 Week 2 4.3 Week 3 4.4 Week 4 4.5 Week 5 4.6 Week 6 4.7 Week 7 4.8 Week 8 4.9 Week 9, 10 4.10 Week 11 4.11 Week 12 4.12 Week 13 4.13 Week 14 4.14 Week 15 5 Suggested Readings 5.1 Week 1 5.2 Week 2 5.3 Week 3 5.4 Week 4 5.5 Week 5 5.6 Week 6 5.7 Week 8 6 Other Online Materials

Course websites

• Course wiki
  • http://lab.zoo.cs.yale.edu/cs445-wiki/
• Yale classes server
  • http://classesv2.yale.edu/
• Previous years' pages:
  • Spring 2007
    • http://www.gersteinlab.org/courses/545/07-spr/
    • http://wiki.gersteinlab.org/pubinfo/index.php/Cs545-07

Homework

• Homework 1
  • assignment
  • Due: Feb 7, 2008.
  • submitted summaries

• Homework 2
  • assignment
  • Due: Feb 21, 2008

• Homework 3
  • assignment
  • hw3_data.csv
  • hints
  • Due: Mar 6, 2008

Final Project

• CPSC445b/545b (2008) Term Projects
  • description
  • One paragraph description due: March 27, 2008 (send to martin.schultz@yale.edu AND jiang.du@yale.edu)
  • Project reports due: April 28, 2008 (send to jiang.du@yale.edu)
  • 15-20 minute project presentations: during class on April 1, 10, 15, 17, 22, 24 (To make reservations for speaking times, please send your preferences to jiang.du@yale.edu)
  • presentation sechedule

Slides

Week 1

• Tu Jan 15, Martin Schultz (MS): Introduction to Data Mining
  • slides

• Thur Jan 17, Jiang Du (JD): Introduction to R
  • slides
  • wine.data wine.r

Week 2

• Tu Jan 22, MS: Introduction to Datamining and Data storage/cleaning

• Thur Jan 24, MS: OLAP, Regression

Week 3

• Tu Jan 29, MS: Multilinear Regression, cross validation
  • slides

• Thur Jan 31, MS: discriminant analysis, perceptrons, SVM
  • slides: SVM
  • additional slides: SVM
  • slides: perceptron models

Week 4

• Tu Feb 5, Mark Gerstein (MG): Bayesian classification
  • slides: Predicting Networks through Bayesian Integration #1 - Theory
  • slides: Predicting Networks through Bayesian Integration #2 - Application

• Thur Feb 7, JD: Decision trees
  • slides
  • example

Week 5

• Tu Feb 12, MS: Logistic regression

• Thur Feb 14, MG: PCA
  • slides: Theory
  • slides: Application

Week 6

• Tu Feb 19, MS: k-nearest neighbors, neural networks
  • link: Hans Rosling's presentation

• Thur Feb 21, MS: Clustering

Week 7

• Tu Feb 26, MS: Mining Time series

• Thur Feb 28, Michael Krauthammer (MK): Text Mining

Week 8

• Tu Mar 4, Songhua Xu (SX): Web, image mining

• Thur Mar 6, MS: Association Analyses

• Thur Mar 6, JDU: Data Mining Packages in R: logistic regression & SVM
  • slides

Week 9, 10

• Spring Break

Week 11

• Tu Mar 25: Max and Kjell: DM Lab (AKW 400)
  • slides
  • Model Building: General Strategies, Data Pre-processing, and Partial Least Squares

• Thur Mar 27: MS

Week 12

• Tu Apr 1: Student presentations

• Thur Apr 3: Max and Kjell: DM Lab
  • Model Building: Ensemble Methods

Week 13

• Tu Apr 8: Max and Kjell: DM lab

• Thur Apr 10: student presentations
  • An Introduction to caret

Week 14

• Tu Apr 15: student presentations

• Thur Apr 17: student presentations

Week 15

• Tu Apr 22: student presentations

• Thur Apr 24: student presentations

Suggested Readings

Week 1

• Super Crunchers
  • Chapters 1-4, pp. 1-102.
  • Keep your eyes open for potential term application oriented projects
• Weka Book (Witten and Frank)
  • Chapter 1, pp. 1-40.

Week 2

• Super Crunchers
  • Chapters 5-6, pp. 103-155.

• Weka Book (Witten and Frank)
  • Chapter 2, pp. 41-60.
  • Chapter 4, Section 4.6, pp. 119-127.

Week 3

• Super Crunchers
  • Chapters 7-8, pp. 156-218.

• Weka Book (Witten and Frank)
  • Chapter 4, Section 4.6, pp. 119-127.
  • Chapter 6, Section 6.3, pp. 214-235.

Week 4

• Weka Book (Witten and Frank)
  • Bayesian Methods: Chapter 4, Section 4, pg. 141, Section 6, pp. 271-283.
  • Decision Trees: Chapter 3, Section 3.3, pp. 62-65, Chapter 4, Section 4.3, pp.97 105.

Week 5

• Weka Book (Witten and Frank)
  • Logistic Regression: Chapter 4, Section 4.6, pg. 121-125.
• PCA, SVD, and LSI
  • pdf

Week 6

• Weka Book (Witten and Frank)
  • K-nearest neighbors (instance-based learning): Chapter 4, Section 4.7, pp. 128-136, Chapter 6, Section 6, pp. 235-243.
  • Neural Networks: Chapter 6, Section 6.3, pp. 223-226, 233.
  • Clustering: Chapter 4, Section 4.8, pp. 136-139, Chapter 6, Section 6.6, pp. 254-271.

Week 8

• Weka Book (Witten and Frank)
  • Bayesian Methods: Chapter 4, Section 4, pg. 141, Section 6, pp. 271-283.
  • Decision Trees: Chapter 3, Section 3.3, pp. 62-65, Chapter 4, Section 4.3, pp.97-105.

Other Online Materials

• Introduction to Data Mining (by Tan et al.)
  • Chapter 4, 6 and 8
  • Chapter 5

• Data Mining (by Graham Williams)
  • Draft Book for use only in this course

• Intro to R and Data Mining (by Luis Torgo)
  • http://www.liaad.up.pt/~ltorgo/DataMiningWithR/

• R Documentation
  • http://www.cran.r-project.org/other-docs.html