Course Logistics

• Instructor: Professor Matt Golub
• Teaching Assistants:
  • Lillian Li (yli2244 at cs.washington.edu)
  • Belle Liu (belleliu at uw.edu)
• Lecture Time: Mondays and Wednesdays, 1:30–2:50pm
• Lecture Location: CSE2 (Gates Center), G04

Course Description

Brains are remarkably complex, massive networks of interconnected neurons that underlie our abilities to intelligently sense, reason, learn, and interact with our world. Technologies for monitoring neural activity in the brain are revealing rich structure within the coordinated activity of these interconnected populations of neurons. In this course, we will discuss machine learning models that can be applied toward (1) understanding how neural activity in the brain gives rise to intelligent behavior and (2) designing algorithms for brain-interfacing biomedical devices. Topics will include basic neurobiology, classical probabilistic machine learning foundations, and modern deep learning approaches, including variational autoencoders and recurrent neural networks. Coursework will include readings from the machine learning and computational neuroscience literature, programming assignments, and a final modeling project applied to neural population data.

Course Goals

The primary goals for the course are to:

• Build practical foundations for developing machine learning models for neuroscience and neuroengineering applications.
• Enable students to ask research-level questions at the intersection of machine learning and neuroscience.
• Introduce the real-world challenges and opportunities around working with experimental neuroscience data.

Prerequisites

• Students entering the class should be comfortable programming in Python and should have pre-existing working knowledge of multivariate calculus (e.g., MATH 126), probability and statistics (e.g., CSE 312 / STAT 390), and linear algebra (e.g., MATH 208).
• Some exposure to machine learning (e.g., CSE 446 / 546) may be helpful for graduate students and is required for undergraduates.
• Prior knowledge of neuroscience is not required.
• For a brief refresher, consider consulting the linear algebra and statistics/probability reference materials on the Resources page.

Grading Breakdown

• 60%: Homework assignments (5 at approximately 12% each)
• 10%: Half-page summaries of approximately 5 papers we will discuss in class
• 10%: In-class participation
• 20%: Final project
• No exams

Registering for the Course

• CSE graduate students can register directly without an add code.
• Undergraduates and non-CSE students can request an add code by filling out this form.
• For assistance with registration or add codes, please reach out to the CSE graduate advisors (grad-advising [at] cs [dot] washington [dot] edu).

Frequently Asked Questions