Joint PhD in Neural Computation & Machine Learning-Machine Learning Department - Carnegie Mellon University

Joint PhD Program in Neural Computation & Machine Learning

This PhD program trains students in the application of machine learning to neuroscience by combining core elements of the ML PhD program and the Program in Neural Computation (PNC) offered by the Center for the Neural Basis of Cognition (CNBC).

During the first year, students will be advised by a faculty member in CNBC and/or MLD. In the second year the student will typically be supported by a research grant to a faculty member, who would become the advisor.

PNC/ML Joint program requirements include four core courses from CNBC and five core courses for ML.


CNBC core course requirements:

Must take at least one of the following courses:

Math 3375 Computational Neuroscience (Univ. of Pittsburgh)
15-833 Computational Models of Neural Systems
36-759 Statistical Models of the Brain
85-719 Intro to Parallel Distributed Processing

Must also gain training in cell and molecular neuroscience/neurophysiology, systems neuroscience,and cognitive science in the following courses:

Neuroscience 2012 Neurophysiology (Univ. of Pittsburgh)
Neuroscience 2102 Systems Neuroscience or Neuroscience 2011 Functional Neuroanatomy (Univ. of Pittsburgh)
85-765 Neuroscience

Machine Learning core course requirements:

10-715 Advanced Introduction to Machine Learning
36-705 Intermediate Statistics
10-702 Statistical Machine Learning

Plus any two of the following:

10-708 Probabilistics Graphical Models
10-725 Convex Optimization
15-826 Multimedia Databases and Data Mining
15-750 Graduate Algorithms or 15-853 Algorithms in the Real World


A typical curriculum is as follows:

FALL - Year One

SPRING - Year One

10-715 Advanced Introduction to  Machine Learning 10-702 Statistical Machine Learning
36-705 Intermediate Statistics One of the following courses:
10-708 Graphical Models
10-725 Convex Optimization
15-826 Multimedia Databases
15-750 Algorithms or 15-853 Algorithms in the Real World
10-915 ML Journal Club

FALL - Year Two

SPRING - Year Two

85-765  Cognitive Neuroscience 03-763 Systems Neuroscience
One of the following courses:
10-708 Graphical Models
10-725 Convex Optimization
15-826 Multimedia Databases
15-750 Algorithms or 15-853 Algorithms in the Real World
36-759 Statistical Models of the Brain

FALL - Year Three

03-762 Advanced Cellular Neuroscience

Experimental Training

Students in the program just spend significant time in the lab of one or more experimentalists in order to gain a detailed understanding of how the experimental data are collected. Students working in a strictly computational lab are required to do a rotation of at least 10 weeks in an experimental lab with the intent to begin (or continue) a collaboration with that lab.
Note: the experimental rotation may serve as a major component of either the first-year or second-year research requirement.

Communication Skills

It is crucial that students develop the ability to communicate effectively, both orally and in writing. Practice speaking will occur during journal clubs and related presentations. In addition, the two research requirements involve both oral and written work.

Program Milestones

First year research requirement:
By the end of the first calendar year in the program, all students will be required to complete a data-analytic project. The purpose of the project is to have the student identify a biological problem, understand the data collection process, articulate the goals of building a model or performing a particular kind of analysis and implement this computational approach.

Second research project:
All students will be required to complete a deeper computational project. The student's work on the project should demonstrate that the student has 1) the ability to analyze and interpret experimental data in a particular area 2) the ability to develop and implement a computational approach incorporating the relevant level of biological detail and 3) the ability to organize, interpret and present the results of the computational work. This project should be a body of work suitable for publication.

Ph.D. Thesis Proposal:
Required coursework should be completed by the end of the third year. During the fourth year a PhD. candidate should present a thesis proposal first to his or her thesis committee and then to the CNBC and MLD community.

Ph.D. Thesis Defense:
Normally the dissertation is completed during the student's fifth year.

Applying to the Joint Program in Neural Computation & Machine Learning

Students who are interested in the joint Ph.D. program in Neural Computation and Machine Learning may apply either through CNBC or the Machine Learning Department. Admission into this program is decided by a joint committee with faculty from both CNBC and the Machine Learning Department.

To apply through CNBC, indicate your interest in the Joint PNC/Machine Learning PhD Program in your personal statement. CNBC Online Application

To apply through the Machine Learning Department, check the box for Joint Neural Computation/Machine Learning PhD Program. Machine Learning Online Application

For questions send email to: pnc-admissions@cnbc.cmu.edu