Joint Ph.D. Program in Neural Computation and Machine Learning
This Ph.D. program trains students in the application of machine learning to neuroscience by combining core elements of the Machine Learning Ph.D. 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 the 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 adviser.
The PNC/ML Joint program requires four core courses from the CNBC and five core courses from the Machine Learning Department.
Requirements and Sample Schedule
CNBC Core Course Requirements
Students must take at least one of the following courses:
- Math 3375: Computational Neuroscience (University of Pittsburgh)
- 15-833: Computational Models of Neural Systems (CMU)
- 36-759: Statistical Models of the Brain (CMU)
- 85-719: Intro to Parallel Distributed Processing (CMU)
Students must also gain training in cell and molecular neuroscience/neurophysiology, systems neuroscience,and cognitive science by taking the following courses:
- Neuroscience 2012 Neurophysiology (University of Pittsburgh)
- Neuroscience 2102 Systems Neuroscience or Neuroscience 2011 Functional Neuroanatomy (University of Pittsburgh)
- 85-765: Neuroscience (CMU)
Machine Learning Core Course Requirements
Students must take the following classes:
- 10-715: Advanced Introduction to Machine Learning
- 36-705: Intermediate Statistics
- 10-716: Advanced Machine Learning — Theory and Methods
And 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
Sample Schedule: Year One, Fall
- 10-715 Advanced Introduction to Machine Learning
- 36-705 Intermediate Statistics
Sample Schedule: Year One, Spring
- 10-702 Statistical Machine Learning
- 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
Sample Schedule: Year Two, Fall
- 85-765 Cognitive Neuroscience
- 10-708: Graphical Models
- 10-725: Convex Optimization
- 15-826: Multimedia Databases
- 15-750: Algorithms or 15-853: Algorithms in the Real World
Sample Schedule: Year Two, Spring
- 03-763: Systems Neuroscience
- 36-759: Statistical Models of the Brain
Sample Schedule: Year Three, Fall
- 03-762: Advanced Cellular Neuroscience
Experimental Training
Students in the program spend significant time in the lab of one or more experimentalists to gain a detailed understanding of how 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 are required to have completed 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 are 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 Ph.D. 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.