EECS 940: Theoretic Foundation of Data Science

[W 8:30-11:00 am, ITTC 250]
Spring 2014

    Schedule & Lectures Assignments Additional Files Syllabus


    • 01/22/2014: First class meeting

    Instructor and Class Information:

    Course Objectives and Overview:

    We will review statistical and mathematical principles that are utilized in the 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.


    EECS 738, EECS 837, EECS 844 or equivalent.

    Textbook (not required but recommended):

    1. All of Statistics, by Larry Wasserman, Springer, 2006, ISBN-13: 978-0387402727.
    2. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2nd edition, 2009, 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; 2nd edition, 2006, ISBN-10: 0387303030, ISBN-13: 978-0387303031