• 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 calculus.

• 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 generalization error.
  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.

• Course Format:
  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 some programming. The actual format of the course will ultimately depend on the number of students enrolled.

Schedule of Classes We meet in Innovation Hall, Rm. 136, M 7:20pm - 10:00pm

• List of papers (UNDER CONSTRUCTION)
  Ensembles of Classifiers
  • R. Polikar, Ensemble Based Systems in Decision Making, IEEE Circuits and Systems Magazine, 6(3), pp. 21-45, 2006.
• L. Breiman, Bagging Predictors, Machine Learning, 26(2), pp. 123-140, 1996.
• L. Breiman, Random Forests, Machine Learning, 45, pp. 5-32, 2001.
• L. Breiman, Pasting small votes for classification in large databases and on-line, Machine Learning, 36, pp. 85-103, 1999.