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This page contains general information for the class:

''CPSC445/CPSC545/MBB334/MBB545/CBB545'' Spring 2008

'''Introduction to Data Mining'''

== 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
** [http://zoo.cs.yale.edu/classes/cs445/hw/CPSC445_hw1_2008-2.doc assignment]
** Due: Feb 7, 2008.
** [[hw1 summaries| submitted summaries]]

* Homework 2
** [http://zoo.cs.yale.edu/classes/cs445/hw/CPSC445_hw2_2008-3.doc assignment]
** Due: Feb 21, 2008

* Homework 3
** [http://zoo.cs.yale.edu/classes/cs445/hw/CPSC445_hw3_2008-2.doc assignment]
** [http://zoo.cs.yale.edu/classes/cs445/hw/hw3_data.csv hw3_data.csv]
** [http://zoo.cs.yale.edu/classes/cs445/hw/hw3_hints.doc hints]
** Due: Mar 6, 2008

== Final Project ==

* CPSC445b/545b (2008) Term Projects
** [http://zoo.cs.yale.edu/classes/cs445/hw/CPSC445_term_projects_2008-v2.doc 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)
** [http://spreadsheets.google.com/pub?key=pXgR9Xs-YQoHGGHqva0_5Lw presentation sechedule]

== Slides ==

=== Week 1 ===

* Tu Jan 15, [http://www.cs.yale.edu/people/schultz.html Martin Schultz] (MS): Introduction to Data Mining
** [http://zoo.cs.yale.edu/classes/cs445/slides/kumar_1.ppt slides]

* Thur Jan 17, [http://homes.gersteinlab.org/people/jiangdu/ Jiang Du] (JD): Introduction to R
** [http://zoo.cs.yale.edu/classes/cs445/slides/data_mining-08spring-Intro2R.ppt slides]
** [http://zoo.cs.yale.edu/classes/cs445/misc/wine.data wine.data] [http://zoo.cs.yale.edu/classes/cs445/misc/wine.r 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
** [http://www.gersteinlab.org/courses/545/07-spr/slides/DM_multiple_regression.ppt slides]

* Thur Jan 31, MS: discriminant analysis, perceptrons, SVM
** [http://www.gersteinlab.org/courses/545/07-spr/slides/DM_SVM.ppt slides: SVM]
** [http://www.gersteinlab.org/courses/545/07-spr/slides/DM_SVM-law.ppt additional slides: SVM]
** [http://www.gersteinlab.org/courses/545/07-spr/slides/DM_perceptron.ppt slides: perceptron models]

=== Week 4 ===

* Tu Feb 5, [http://bioinfo.mbb.yale.edu/about Mark Gerstein] (MG): Bayesian classification
** [http://www.gersteinlab.org/courses/545/07-spr/slides/cbb545b-spr07-bioinfo3-bayes1.ppt slides: Predicting Networks through Bayesian Integration #1 - Theory]
** [http://www.gersteinlab.org/courses/545/07-spr/slides/cbb545b-spr07-bioinfo4-bayes2.ppt slides: Predicting Networks through Bayesian Integration #2 - Application]

* Thur Feb 7, JD: Decision trees
** [http://zoo.cs.yale.edu/classes/cs445/slides/DM_DecisionTree-mod_jdu.ppt slides]
** [http://www.cise.ufl.edu/~ddd/cap6635/Fall-97/Short-papers/2.htm example]

=== Week 5 ===

* Tu Feb 12, MS: Logistic regression

* Thur Feb 14, MG: PCA
** [http://www.gersteinlab.org/courses/545/07-spr/slides/cbb545b-spr07-bioinfo5-svd1.ppt slides: Theory]
** [http://www.gersteinlab.org/courses/545/07-spr/slides/cbb545b-spr07-bioinfo6-svd2.ppt slides: Application]

=== Week 6 ===

* Tu Feb 19, MS: k-nearest neighbors, neural networks
** [http://www.gapminder.org/video/talks/ted-2007---the-seemingly-impossible-is-possible.html link: Hans Rosling's presentation]

* Thur Feb 21, MS: Clustering

=== Week 7 ===

* Tu Feb 26, MS: Mining Time series

* Thur Feb 28, [http://www.yalepath.org/faculty.lasso?id=KrauthammerM Michael Krauthammer] (MK): Text Mining

=== Week 8 ===

* Tu Mar 4, [http://www.cs.hku.hk/~songhua/ Songhua Xu] (SX): Web, image mining

* Thur Mar 6, MS: Association Analyses

* Thur Mar 6, JDU: Data Mining Packages in R: logistic regression & SVM
** [http://zoo.cs.yale.edu/classes/cs445/slides/r_pkgs-jdu.ppt slides]

=== Week 9, 10 ===

* Spring Break

=== Week 11 ===

* Tu Mar 25: Max and Kjell: DM Lab (AKW 400)
** [http://zoo.cs.yale.edu/classes/cs445/slides/Pfizer_Yale_Version.ppt slides]
** [http://zoo.cs.yale.edu/classes/cs445/slides/Yale1.pdf 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
** [http://zoo.cs.yale.edu/classes/cs445/slides/Yale2.pdf Model Building: Ensemble Methods]

=== Week 13 ===

* Tu Apr 8: Max and Kjell: DM lab

* Thur Apr 10: student presentations
** [http://zoo.cs.yale.edu/classes/cs445/slides/caret.pdf 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 ===

* [http://www.randomhouse.com/bantamdell/supercrunchers/ Super Crunchers]
** Chapters 1-4, pp. 1-102.
** Keep your eyes open for potential term application oriented projects
* [http://www.cs.waikato.ac.nz/~ml/weka/book.html Weka Book] (Witten and Frank)
** Chapter 1, pp. 1-40.

=== Week 2 ===

* [http://www.randomhouse.com/bantamdell/supercrunchers/ Super Crunchers]
** Chapters 5-6, pp. 103-155.

* [http://www.cs.waikato.ac.nz/~ml/weka/book.html Weka Book] (Witten and Frank)
** Chapter 2, pp. 41-60.
** Chapter 4, Section 4.6, pp. 119-127.

=== Week 3 ===

* [http://www.randomhouse.com/bantamdell/supercrunchers/ Super Crunchers]
** Chapters 7-8, pp. 156-218.

* [http://www.cs.waikato.ac.nz/~ml/weka/book.html Weka Book] (Witten and Frank)
** Chapter 4, Section 4.6, pp. 119-127.
** Chapter 6, Section 6.3, pp. 214-235.

=== Week 4 ===

* [http://www.cs.waikato.ac.nz/~ml/weka/book.html 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 ===

* [http://www.cs.waikato.ac.nz/~ml/weka/book.html Weka Book] (Witten and Frank)
** Logistic Regression: Chapter 4, Section 4.6, pg. 121-125.
* PCA, SVD, and LSI
** [http://www.cs.pitt.edu/~milos/courses/cs3750/Readings/Berry_etal-1999.pdf pdf]

=== Week 6 ===

* [http://www.cs.waikato.ac.nz/~ml/weka/book.html 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 ===

* [http://www.cs.waikato.ac.nz/~ml/weka/book.html 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.)
** [http://www-users.cs.umn.edu/~kumar/dmbook/index.php Chapter 4, 6 and 8]
** [http://zoo.cs.yale.edu/classes/cs445/misc/chap5_other_classification.pdf Chapter 5]

* Data Mining (by Graham Williams)
** [http://zoo.cs.yale.edu/classes/cs445/misc/mar13lae08.pdf 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

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