## Data Mining: A Mathematical Perspective

## CS 391D

### Unique No. 50761

Spring 2020

Fri 10am-1pm

GDC 3.516

Instructor: Prof. Inderjit Dhillon
(send email)

Office: GDC 4.704

Office Hours: Fri 9-10am and by appointment

Guest Lecturer: Ali Jalali (send email)

TA: Xingchao Liu
(send email)

Office: GDC 4.802C

Office Hours: TTh 3-4:30pm
### 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 and machine learning. 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 student presentations and
projects; projects can vary in their theoretical/mathematical
content, and in the implementation/programming involved.
Projects will be conducted by teams of 2-4 students.

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

### Reference Books

### Class Presentations

### Class Projects

### Syllabus

### Lecture Notes

### Grading

### Handouts