Spring 2009 CS-795: Ensemble Based Systems in Decision Making [INFS-795, IT-803]
Prerequisites: INFS-755, or equivalent knowledge.
Some programming experience is expected.
Students should be familiar with
basic probability and statistics concepts, linear algebra, optimization, and multivariate
General Description and Preliminary List of Topics:
This course is about combining the "opinions" of an ensemble of experts (e.g., classifiers) with the objective of
computing a new emerging "opinion" that is better than the individual ones.
The task of improving classification accuracy by learning ensembles of classifiers is considered
as one of the most important directions in machine learning research.
Recent empirical work has shown that combining predictors can lead to significant reductions in
We will discuss popular ensemble
methods such as bagging, boosting, and AdaBoost. We will study the conditions under which combining multiple
experts is beneficial; how to construct the individual components; how to select a subset of "good" experts
from a large pool of possible components; and how to generate a consensus response from those of the individual
members. We will consider ensembles in which the components are supervised learners, unsupervised ones, or
learners with constraints. Challenges in each scenario will be discussed.
Material from books and research papers published in major conferences and journals will be
studied in depth. The course will include lectures by the instructor,
presentations by students, and discussions. Students are required to study
the material covered in class. No textbook is required. Research papers, and handouts will
be made available.
Grading will be based on homework assignments,
presentations, and a project. Homeworks will require
The actual format of the course will ultimately depend on the number of
Schedule of Classes
We meet in Innovation Hall, Rm. 136, M 7:20pm - 10:00pm
List of papers (UNDER CONSTRUCTION)
Ensembles of Classifiers