Jayhawk


EECS 738: Machine Learning


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


    Schedule & Lectures Assignments Additional Files Syllabus


    Announcements:

    • 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.

    Prerequisites:

      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