EECS 738: Machine Learning

[M/W/F 1:00-1:50 pm,  LEA1136 Fall 2015]

    Schedule & Lectures Assignments Additional Files Syllabus


    • 08/24/15: First class meeting

    Instructor and Class Information:

      Instructor: Luke Huan

      Email: jhuan AT ku dot edu
      Office: Eaton 2034
      Office Hours: F 2:00-3:00
      Phone: 864-5072
      Lectures: M/W/F 1:00-1:50, LEA 1136
      Web: http://ittc.ku.edu/~jhuan/EECS738_F15

    Course Objectives and Overview:

         Machine learning is the study of computer algorithms that improve automatically through experience" (Tom Mitchell). This course introduces basic concepts and algorithms in machine learning. A variety of topics such as Bayesian decision theory, dimensionality reduction, clustering, neural networks, hidden Markov models, combining multiple learners, reinforcement learning, Bayesian learning etc. will be covered.


      Graduate standing in CS or CoE or consent of the instructor

        Text books:
      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
      5. Machine Learning: A Probabilistic Perspective, Kevin Murphy, The MIT Press (August 24, 2012), ISBN-10: 0262018020, ISBN-13: 978-0262018029