Data Mining: A Mathematical Perspective

CS 391D/CSE 392

CS Unique No. 52580 / CSE Unique No. 65610

Fall 2011
TTh 9:30-11am
PAI 3.14

Instructor: Prof. Inderjit Dhillon (send email)
Office: ACES 2.332
Office Hours: Tue 11am-noon and by appointment
TA: Nagarajan Natarajan (send email)
Office: ACES 5.302
Office Hours: MF 10:30am-noon

Course Description

Data mining is the automated discovery of interesting patterns and relationships in massive data sets. This graduate course will focus on various mathematical and statistical aspects of data mining. Topics covered include supervised methods (regression, classification, support vector machines) and unsupervised methods (clustering, principal components analysis, non-linear dimensionality reduction). The technical tools used in the course will draw from linear algebra, multivariate statistics and optimization. The main tools from these areas will be covered in class, but undergraduate level linear algebra is a pre-requisite (see below). A substantial portion of the course will focus on research projects, where students will choose a well defined research problem. Projects can vary in their theoretical/mathematical content, and in the implementation/programming involved. Projects will be conducted by teams of 3-4 students.

Pre-requisites: Basics (undergraduate level) of linear algebra (M341 or equivalent) and some mathematical sophistication.

Reference Books

  • "Pattern Recognition and Machine Learning" by C. Bishop, Springer, 2006.
  • "Elements of Statistical Learning: Data Mining, Inference, and Prediction" by T. Hastie, R. Tibshirani, J. Friedman, Springer-Verlag, 2001.
  • "Pattern Classification" by R. Duda, P. Hart and D. Stork, John Wiley and Sons, 2000.
  • Grading

  • 10 + 35 = 45% Class Project (First submission + Final submission)
  • 25% Homeworks
  • 25% Midterm
  • 5% Class participation and attendance
  • Code of Conduct