Masters Program in Machine Learning
The curriculum for the ML Masters is built on a foundation of five core courses and two 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 Machine Learning, 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.
Plus any two of the following:
- 10-708 Probablistic Graphical Models
- 10-725 Convex Optimization
- 15-826 Multimedia Databases and Data Mining
- 15-750 Algorithms or 15-853 Algorithms in the Real World
Data Analysis Project Requirement, in the second year, which serves in lieu of an MS thesis.
The two electives may be chosen from the List of electives. Other electives may be considered after consultation with the student's advisor and if approved by the co-directors.
Double Counting Courses:
Any course counted toward another master-level degree may not be counted toward our Master in Machine Learning.
The Data Analysis Project requirement:
The final requirement is for the student to demonstrate data mining skills in the context of a focused project. The Data Analysis Project (DAP) 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 a presentation in the ML Journal Club and also the submission of a DAP Paper. Passing this requirement will be the judgment of the DAP committee.
Student must form an official "DAP committee" of three faculty to evaluate the document. The committee will consist of the advisor, the Journal club instructor(s), and one other faculty member selected by the student. The third member is typically someone with an interest in the analysis of the data set, and does not have to be an expert in ML or part of the student's thesis committee.The student should form the committee as early as possible during the DAP research process, and inform Diane of who the members are. Two faculty from the committee are required to attend the presentation.
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 Data Analysis Project (DAP), or to collaborate for any other reason.