Machine learning is an interdisciplinary research area which concerns with development of computationally efficient statistical techniques for understanding of patterns in large data sets. This intent of this reading group is to discuss the most recent developments in this vibrant research field.

This series of discussions is geared mostly to graduate students in sciences and engineering with research interests in machine learning. We also welcome participation from postdocs and faculty with similar interests.

A graduate level course in machine learning or data mining, permission of instructor.

The group will be organized as a series of participants’ presentations of the preselected recent papers from the top machine learning venues (e.g., ICML, UAI, NIPS, JMLR, MLJ). Each meeting will consist of a presentation of 1-2 papers followed by a group discussion of the main ideas.

We will meet for one hour once per week (except for holidays). Each participant is expected to present at least once during the semester.

The group is offered as a 1-unit course in the Statistics department, and Purdue student participants irrespective of their department are expected to enrolled in the course. The course is in the process of cross-listing in the CS department.

Course number: STAT 69500 (54849) or CS 59100 (55858)
Instructor: Sergey Kirshner
Time/place: Wednesdays, 1:30-2:20pm, B206 Beering Hall
Mailing list: fall-2011-stat-69500-sk1@lists.purdue.edu

Enrolled students will be evaluated (P/NP) based on their presentation and overall participation.