MATH 574M - Statistical Machine Learning and Data Mining


Announcements
  • First class on 01/10.


    Course Information
    Lectures: Tue. and Thu. 9:30-10:45am, Bio. Sci. West 210| Syllabus
    Office Hours: Tuesday 2-3pm, ENR2 S323. Or by appointment.
    TA Office Hours: Thursday 12:30-1:30pm, Friday 2-3pm, Math 507. Or by appointment.
    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

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

    Course Activities
    Week 1 (Jan 16-20) Read Chapter 1: Overview of Data Mining Lecture 1: Introduction
    Get Familiar with Software: Intrudction to R R Brief Intro, R Guide For Reginners
    Supplementary Reading: Data mining and statistics: what is the connection? Friedman (1997) Homework 1. Assigned on Jan 21, due on Feb 4.
    Week 2-3 (Jan 21- Feb 2) Read Chapter 2: Theory of Supervised Learning Lecture 2: Statistical Decision Theory (I)
    Lecture 3: Statistical Decision Theory (II)
    Week 4 (Feb 3 -Feb 9) Read Chapter 4.2-4.4: Linear Classificaton Methods for Binary Problems Lecture 4: Binary Classification (I): Basics
    Homework 2 Assignment. Assigned on Feb 4, due on Feb 18.
    Week 5 (Feb 10 - Feb 16) 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
    Homework 2 Solution, Code
    Week 6 (Feb 17 - Feb 23) Curse of Dimensionality; Linear Binary Classification for High Dimensional Problems Lecture 6: Binary Classification (III): Extension to High Dimensional Classification Problems
    Homework 3 PDF file, Latex File. Assigned on Feb 25, due on March 17.
    Read Chapter 4.1: Nonlinear Classification Methods Lecture 7: K nearest neighbor (Knn) methods
    Week 7-8 (Feb 24 - Mar 7) Topic: Introduction to Multiclass Classification Lecture 8: Multiclass Classification
    Homework 3 Solution, Code
    Week 9 (March 8 - Mar 22) Topic: Nonlinear Discriminant Analysis Lecture 9: QDA and RDA
    Homework 4 PDF file, Latex File. Assigned on March 19, due on April 2.
    Week 10 (March 23 - Mar 29) Supplementary Reading: LDA for improved large vocabulary continuous speech recognition Lecture 10: PCA
    Read Chapter 3: Linear Regression Lecture 11: Linear Regression
    Supplementary Reading: Linear Model Theory
    Week 11 (March 30 - April 5) Read Chapter 3 : Variable Selection for Linear Regression Lecture 12: Variable Selection (I)
    Reading: Regression Shrinkage and Selection via the LASSO, Final Project: Project assigned on March 30, due on May 19
    Final Project Suggested Reference List
    Supplementary Reading: Regularization and variable selection via the elastic net Lecture 13: Shrinkage Methdods by LASSO


    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.