Segment

Topic

Due(Date)

Readings

Personal Profile and Weka Installation

July 4

Hands On Assignment 1

Data Mining Profile Due

Pre: Online Research

June 15 – June 3

Literature Review and Research Question Selection

July 8

Written Assignment 1:

2 page proposal

Face to Face:

July 6 – July 17

1

KDD – The Lay of The Land

July 6

Required Reading:

Usama Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth. "The KDD process for extracting useful knowledge from volumes of data", Communications of the ACM. Volume 39 , Issue 11 (November 1996). ACM Press New York, NY, USA

Recommended Reading:

R.O. Duda, P.E. Hart and D.G. Stork. “Pattern Classification”, Wiley-Interscience, 2001. Chapter 1: Introduction

2

Some Methods for Classification Part I

WEKA Lab

July 7

HW Due: 

Hands on Assignment 2

Recommended Readings:

R.O. Duda, P.E. Hart and D.G. Stork. “Pattern Classification”, Wiley-Interscience, 2001.

Chapter 8.1-8.4: Decision Trees

Chapter 6: Neural Networks

Tom Mitchell, Machine Learning, McGraw Hill, 1997.

Chapter 6: Bayesian Learning

Chapter 10: Learning Sets of Rules

3

Some Methods for Classification Part II

July 8

HW Due:

Paper Summaries

Required Readings:

Pedro Domingos and Michael Pazzani., On the Optimality of the Simple Bayesian Classifier under Zero-One Loss Machine Learning, 29, 103-130, 1997

Perlich, C., F. Provost, and J. Simonoff.  "Tree Induction vs. Logistic Regression: A Learning-curve Analysis."  To appear in the Journal of Machine Learning Research.  CeDER Working Paper #IS-01-02, Stern School of Business, New York University, NY, NY 10012.  Fall 2001.

On Discriminative vs. Generative Classifiers: A comparison of logistic regression and Naive Bayes. Andrew Y. Ng and Michael Jordan. To appear in NIPS 14, 2002.

Eibe Frank and Ian H. Witten (1998). Generating Accurate Rule Sets Without Global Optimization. In Shavlik, J., ed., Machine Learning: Proceedings of the Fifteenth International Conference, Madison, Wisconsin. Morgan Kaufmann Publishers, San Francisco, CA, pp. 144-151.

4

Genetic Algorithms/Association Rules

July 9

HW Due:

Paper Summaries

Hands on Assignment 3

Required Readings:

Cooper, L. G., and G. Giuffrida Turning data mining into a management science tool: New algorithms and empirical results, Journal of Management Science, 2000

Padmanabhan, B. and Tuzhilin, A.,Small is Beautiful: Discovering the Minimal Set of Unexpected Patterns, 2000. Procs. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining  pages 54-64, August 2000.

Padmanabhan, B., Zheng, Z., and Kimbrough, S., Personalization from Incomplete Data: What you don’t know can hurt,  2001 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Zheng, Z., Padmanabhan, B., and Kimbrough, S., On the Existence and Significance of Data Preprocessing Biases in Web Usage Mining, 2002.

5

Evaluation

July 10

HW Due:

Paper Summaries

Required Readings:

Provost, F. and T. Fawcett, "Robust Classification for Imprecise Environments." Machine Learning 42, 203-231, 2001

D. Jensen and P.R. Cohen. “Multiple comparisons in induction algorithms”, Machine Learning 38(3) 1999

R. Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. In IJCAI 1995, 1995

6

OLAP/Traditional Tools

July 13

HW Due:

Paper Summaries

Hands on Assignment 4

 TBD

7

Relational Learning

July 14

HW Due:

Paper Summaries

Required Readings:

R. Quinlan and R.M. Cameron-Jones. Induction of logic programs: FOIL and related systems. New Generation Computing, 13:287 312, 1995. I4.

Sean Slattery and Mark Craven. Combining statistical and relational methods for learning in hypertext domains. In Proceedings of the 8th International Conference on Inductive Logic Programming, ILP-98, pages 38 52, 1998.

Neville, J., D. Jensen, L. Friedland and M. Hay. (2002) Learning Relational Probability Trees. University of Massachusetts Amherst, Technical Report 02-55. Revised version February 2003.

D. Koller and A. Pfeffer. Probabilistic frame-based systems. In Proc. AAAI, 1998.

Perlich, C. and F. Provost.  "Aggregation-Based Feature Invention for Relational Learning."  Under Review for SIGKDD 2003. Preliminary version: CeDER Working Paper #IS-03-03, Stern School of Business, New York University, NY, NY 10012.  Spring 2003.

8

Text Mining/Business Intelligence

July 15

HW Due:

Paper Summaries

Recommended Readings:

R. Baeza–Yates and B. Ribeiro-Neto. Modern Information Retrieval, Addison-Wesley 1999.

Chapter 2.5:Classic Information Retrieval

Chapter. 7; Text Operations

Required Readings:

Daniel Billsus and Michael Pazzani, "A Personal News Agent that Talks,Learns and Explains", Proceedings of the Third International Conference

on Autonomous Agents, 1999.

Dwi H. Widyantoro and Thomas R. Ioerger and John Yen, "An AdaptiveAlgorithm for Learning Changes in User Interests", Proceedings of the Eigth International Conference on Information and Knowledge Management, 1999.

Good, N., Schafer, J.B., Konstan, J., Borchers, A., Sarwar, B.,

Herlocker, J., and Riedl, J., Combining Collaborative Filtering withPersonal Agents for Better Recommendations. Proceedings of the 1999 Conference of the American Association of Artificial Intelligence (AAAI-99).l

9

Technical IS Research Methods/Applied Data Mining in Developing Nations

July 16

HW Due:

Paper Summaries

Hands on Assignment 5

Required Readings:

Hevner, A., S. March, J. Park, and S. Ram, "Design Science Research in Information Systems," Working Paper, Carlson School School of Management, University of Minnesota, Minneapolis, MN, 2001.  (most recent version available from Al Hevner)

Weber, R., "Toward a Theory of Artifacts: A Paradigmatic Base For Information Systems Research," Journal of Information Systems, Spring, 1987.

Langley, P., "Crafting Papers on Machine Learning," Journal of Information Systems, Spring, 1987.

10

Wrap Up - Draft Paper Presentations

July 17

Constructive criticism on project presentations

Post: Assignment Submissions

July 31

Hands on Assignment 6: Comparison of methods on given dataset

August 14

Literature Reivew: Summarizing current literature on Data Mining/Machine Learning in developing countries

Post: Online Practice

Term Paper writeup and workshop

Term Paper submission

August 28

Final Term Paper