Secondary Masters in Machine Learning
This program is only available to current Carnegie Mellon PhD students, faculty, and staff.
The extraordinary spread of computers and online data is changing forever the way that important decisions are made in many organizations. Hospitals now analyze online medical records to decide which treatments to apply to future patients. Banks analyze past financial records to learn to spot future fraud, while factories analyze past operations to learn to produce higher quality goods.
While a first generation of data mining algorithms has already been developed and is gaining widespread commercial use, we expect future advances to dramatically increase the role of data mining throughout society. Today's demand for data mining expertise far exceeds the supply, and this imbalance will become more severe over the coming decade. Unfortunately, current degree programs in traditional disciplines fail to provide the kind of curriculum needed to train tomorrow's leaders in this area. The problem is that data mining requires a rich, interdisciplinary education combining topics such as advanced machine learning algorithms, statistical principles that provide the foundations for these algorithms, database and data warehousing methods, complexity analysis, approaches to data visualization, privacy and security issues, and specific application areas such as business, marketing, and public policy.
Carnegie Mellon University's Master's program in Machine Learning is designed to train students to become tomorrow's leaders in the rapidly growing area of data mining. This program will build on Carnegie Mellon's Machine Learning Department which has assembled a multi-disciplinary team of faculty and students across several academic departments, dedicated to producing the next generation of data mining methods.
By exposing students to this combination of interdisciplinary coursework, hands-on applications, and cutting-edge research, we expect our graduates will be uniquely positioned to pioneer new data mining efforts, and to pursue top notch research on the next generation of data mining tools, algorithms, and systems.
The curriculum for the ML Master's is built on a foundation of five core courses and two electives plus a Data Analysis Project.
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.
These 3 set core courses together provide a foundation in machine learning, statistics, probability, and algorithms:
- 10-701 Introduction to Machine Learning or 10-715 Advanced Introduction to Machine Learning
- 10-702 Statistical Machine Learning
- 36-705 Intermediate Statistics
Students also take any 2 of the following menu core courses:
- 10-703 Deep Reinforcement Learning or 10-707 Topics in Deep Learning
- 10-708 Probabilistic Graphical Models
- 10-725 Convex Optimization
- 15-750 Algorithms or 15-853 Algorithms in the Real World
- 15-780 Graduate Artificial Intelligence
- 15-826 Multimedia Databases and Data Mining
- 36-752 Advanced Probability
The 2 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.
Double Counting Courses:
Any course counted toward another master-level or bachelor-level degree may not be counted toward our Secondary Master's in Machine Learning. If a course is counted toward your PhD degree it may also be counted in our Secondary Master's in Machine Learning, so long as such double-counting is permitted by your PhD department.
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 may be carried out either at Carnegie Mellon or at a sponsoring corporate institution under the joint supervision of the sponsor and an ML Core Faculty member. It will be concluded by a written report (in lieu of a Master's 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 brief oral presentation, a poster presentation, 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 two or three members to evaluate the document. The committee will consist of the advisor (who must be Machine Learning Core Faculty), another regular faculty member, and an optional third 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 the Master's Program Coordinator, Dorothy Holland-Minkley, of who the members are. Two faculty from the committee are required to attend the presentation.
Admission Requirements & Application Information
If you are currently enrolled in a Ph.D. program or are staff/faculty at Carnegie Mellon, you may apply for a Secondary Master's in Machine Learning.
Read the following instructions carefully and make certain that you have met all requirements before you submit your application. All materials must be received by the Program Coordinator in order to be considered for admission. There is no application fee. Applications must be submitted before beginning the research course for the Data Analysis Project (DAP) to ensure the project is sufficiently relevant to Machine Learning. This would be either the DAP Prep course or the one year ADA course in Statistics (Note: the ADA in Statistics is only open to PhD students in Statistics). We encourage students to apply as soon as they have completed 10-701 and 36-705.
- Take 10-701 Machine Learning & 36-705 Intermediate Statistics and receive a B+ or better in both courses. (These courses must be taken before applying; they are required for the Secondary Master's degree, and provide a way for students to make sure that they are able to do well in our program before committing to take the entire course load. Students are encouraged to apply as soon as they have taken these courses.)
- Complete the application form. Application [.pdf] If you need more space for your answers, attach additional sheets of paper.
- Prepare a Research Statement. On a separate sheet of paper, type a one-page concise description of a research project that would be appropriate for a DAP.
- Prepare a Statement of Purpose. On a separate sheet of paper, type a one- or two-page concise statement of purpose in the following form:
- Briefly state your objective in pursuing a Master's degree in Machine Learning.
- Describe your background in fields particularly relevant to your objective. List here any relevant academic or industrial experience.
- Include any additional information you wish to supply to the Admissions Committee.
- Request a nomination letter from your advisor or supervisor. For PhD students, this nomination should be completed by your advisor in your home department. For staff, it should be completed by your immediate supervisor. The nomination should be sent directly to the Program Coordinator by the person completing it. The nomination may be a short email stating that your advisor or supervisor supports your decision to pursue the Secondary MS, instead of a formal letter of recommendation.
- Request an endorsement from your prospective DAP advisor (who must be Machine Learning Core Faculty), of the form, "I have reviewed X's research statement and am happy to supervise this DAP research." This endorsement should be emailed directly to the Program Coordinator by the prospective DAP Advisor.
- Include a current resume.
- Include the test score reports from your previous application (GREs and, if applicable, TOEFL records). These can usually be obtained from your home department if you no longer have a copy.
- Include your current Carnegie Mellon transcript or unofficial academic record.
Fall applications are due by October 12 and spring applications are due by March 22.
All materials, including nomination and endorsement letters, must be received by the application deadline for consideration that semester. The Program Coordinator will store any materials submitted early until the application deadline.
Email or deliver all materials to:
Dorothy Holland-Minkley (firstname.lastname@example.org)
Master's Programs Coordinator
Machine Learning Department
Carnegie Mellon University