MATH 574M - Statistical Machine Learning and Data Mining


Announcements
  • First class on 08/25/2020.


    Course Information
    Lectures: Tue. and Thu. 9:30-10:45am, D2L Online | Syllabus
    Office Hours: Tuesday 2-3pm, ENR2 S323. Or by appointment.
    TA Office Hours: TBA.
    Textbooks: The Element of Statistical Learning:data miming, inference, and prediction Hastie, Tibshirani, and Friedman (2001).
    Reference Books:
  • Principle and Theory for Data Mining and Machine Learning by Clark, Forkoue, Zhang (2009)
  • Pattern Recognition and Neural Networks by B. Ripley (1996)
  • Learning with Kernels by Scholkopf and Smola (2000)
  • The Nature of Statistical Learning Theory by Vapnik (1998)
  • An overview of statistical learning theory, Vapnik (1999)

    Useful Links:
  • Kernel Machines
  • Hastie's Software and Data

    R Resouces:
  • Download R (CRAN)
  • Introduction to R | R for Beginners | R reference card
  • Introduction to RStudio

    Statistics Prerequisites:
  • Basic Topics | Joe Watkins' 363 Notes | Joe Watkins' MATH 464 Notes

    Course Activities
    Week 1-2 (August 24-Sep 6) Read Chapter 1: Overview of Data Mining Lecture 1: Introduction
    Get familiar with R and RStudio R Intro, RStudio Intro
    Supplementary Reading: Data mining and statistics: what is the connection? Friedman (1997) Homework 1 PDF, LaTex. Assigned on August 25, due on Sep 8.
    Week 3 (Sep 7 - Sep 13) Read Chapter 2: Theory of Supervised Learning Lecture 2: Statistical Decision Theory (I)
    Lecture 3: Statistical Decision Theory (II)
    Homework 2 PDF, Latex. Assigned on Sep 10, due on Sep 29.
    Week 4 (Sep 14 - 20 ) Read Chapter 4.2-4.4: Linear Classification Methods for Binary Problems Lecture 4: Binary Classification (I): Basics
    Week 5 (Sep 21 - Feb 27) Supplementary Reading: Choosing Between Logistic Regression and Discriminant Analysis, Press, S. and Wilson, S. (1978) Lecture 5: Binary Classification (II): Logistic Regression and Discriminant Analysis


    Auditing
  • Auditors are expected to attend class regularly and submit homework on the same schedule as the other students.

    Policy on Academic Integrity
  • The University policy on academic integrity is spelled out in UA Code of Student Conduct.

    Students with Disabilities
  • Reasonable accommodations will be made for students with verifiable disabilities.