Instructors: Tina Eliassi-Rad and Michael Littman
Date/Time: Wednesdays from 11 AM to 12:30 PM
Place: CoRE 301
Description: In the era of big data, machine learning is ubiquitous. This light seminar will explore state-of-the-art research in machine learning and its applications in various domains. Each week, we will either listen to an invited speaker (from the Rutgers-Yahoo! Machine Learning Seminar) or discuss selected articles.
Grading: Grades for this seminar will be based on upon paper presentation and class participation.
|January 19||Efficient Bayesian Methods for Hierarchical Clustering||Katherine A. Heller|
|January 26||Topic Models We Can Believe In: New Approaches to Evaluating Latent Variable Models for Text Analysis||David Mimno|
|February 2|| Discuss:
Tutorial at The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009.
Presentation is available at our Sakai site.
|February 9|| Discuss:
|February 16|| Discuss:
|February 23||Machine Learning Algorithms for Real Data Sources, with Applications to Climate Science||Claire Monteleoni|
Location: CoRE Auditorium (Room 101)
|Learning Feature Hierarchies for Vision||Yann LeCun|
|March 9||Comprehensive Patient Similarity Learning||Jimeng Sun|
|March 16||Spring Break||---|
|March 23||Inferring the Structure and Scale of Modular Networks||Jake Hofman|
|March 30||Socially Intelligent Machine Learning||Haym Hirsh|
|April 6||Collective Graph Identification||Lise Getoor|
|April 13||Some Remarks on the Model Selection Problem||Branden Fitelson|
|April 20||How ML Relates Victoria Stilwell to George Lucas||David L. Roberts|
|April 27||Computational Insights into Population Biology||Tanya Berger-Wolf|
|May 4||Recommender Systems: The Art and Science of Matching Items to Users||Deepak Agarwal|