Course Requirements

The curriculum for the ML Masters is built on a foundation of five core courses and three electives (plus the Data Analysis Project requirement).

The ML curriculum joins courses with a Computer Science main theme and those with a Probability and Statistics main theme. These may be grouped, as follows:

In CS, relevant sub-fields include: Databases; Machine Learning, Data Mining and algorithms applications in areas such as Robotics, Information Retrieval and AI.

In Statistics (including Philosophy), the sub-fields include: Statistical modeling (e.g., hierarchical and times series); Bayes' Nets, Causation, and experimental design. The curriculum is based on core academic courses on Intermediate Statistics, Machine Learning, Statistical Approaches for Learning & Discovery, Multimedia Databases, and Algorithms.

The five core courses provide, respectively: a secure foundation in mathematical statistics, a survey of basic machine learning techniques with numerous applications; the statistical and probabilistic theoretical underpinnings for these techniques; an introduction to databases for data mining, and a study of advanced algorithms.

10-701 Machine Learning
10-702 Statistical Machine Learning
10-705 Intermediate Statistics
15-826 Multimedia Databases and Data Mining
15-750 Algorithms
10-910 Data Analysis Project Requirement, in the second year, which serves in lieu of an MS thesis.

The three electives may be chosen, in consultation with the student's advisor,to meet the interdisciplinary distribution requirements. List of electives. For a full list of available courses consult with your Advisor.

The Data Analysis Project requirement: 10-910 (It is optional to register for the course.)
The final requirement if for the student to demonstrate data mining skills in the context of a focused project. The Data Analysis Project may be carried out either at Carnegie Mellon or at a sponsoring corporate institution under the joint supervision of the sponsor and a ML faculty. It will be concluded by a written report (in lieu of a Masters Thesis) in which the student demonstrates an ability to approach data mining problems in a way that cuts across existing disciplinary boundaries. The requirement includes giving a ML colloquium on the Data Analysis Project report. Passing this requirement will be the judgment of the ML faculty, under the advice of the faculty advisor for the project.

Research

It is expected that all Masters students engage in active research from their first semester. Moreover, advisor selection occurs in the first month of entering the Masters program, with the option to change at a later time. Roughly half of a student's time should be allocated to research and lab work, and half to courses until these are completed.

This Research page is a list of some of the projects for which ML faculty may be interested in recruiting students. Within each project there can be lines of research which range in size from a semester's work to an entire thesis (or beyond). So, this page is intended as a resource for students looking for a thesis advisor, for a KDD project, or to collaborate for any other reason.

Secondary Masters Student Handbook