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EECS 940 Theoretic Foundation of Data Analysis

Instructor:

Name: Dr. Luke Huan

Meeting Hours: 12:00-3:00 ITTC 250Office Hour: 11:00-12:00 ITTC 340

Phone: 864-5072Email: jhuan@ku.edu

Class Web Page: http://people.eecs.ku.edu/~jhuan/EECS940_S12

Class Objectives:

We will review statistical and mathematical principles that are utilized in data mining and

machine learning research. Covered topics include asymptotic analysis of parameter

estimation, sufficient statistics, model selection, information geometry, function

approximation and Hilbert spaces.

Prerequisite: EECS 738, EECS 837, EECS 844 or equivalent.

Text Book:

Recommended (not required):

1. All of Statistics, by Larry Wasserman, Springer, ISBN-10: 0387402721ISBN-13: 978-0387402727

2. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, byTrevor Hastie, Robert Tibshirani, Jerome Friedman, Springer, ISBN-13: 978-

0387952840.3. Pattern Recognition and Machine Learning, By Christopher Bishop, Springer,

2nd printing edition, 2007, ISBN-10: 0387310738, ISBN-13: 978-0387310732

4. Numerical Optimization, by Jorge Nocedal and Stephen Wright, Springer; 2ndedition, 2006, ISBN-10: 0387303030, ISBN-13: 978-0387303031

Grading:

Homework assignments 20pts

One in-class presentation 30pts

Final Project: 30ptsIn-class discussions 20pts

Total: 100pts

We will use the following scale to assign final grades (tentative and curving will be used):

A: over 90%

B: 80% - 89%

C: 70% - 79%

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