Course Logistics
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.
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 & statistics (e.g., CSE 312/STAT390), linear algebra (e.g., MATH 308).
- Some exposure to machine learning may be helpful but is not required (e.g., CSE 446/546).
- Prior knowledge of neuroscience is NOT required.
- For a brief refresher, consider consulting the linear algebra and statistics/probability reference materials on the Textbooks page.
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.
Grading Breakdown:
10%: Brief 1/2 page summaries of ~5 papers we will discuss in class.
10%: Group presentation, leading discussion of one of the ~5 papers mentioned above.
60%: Homework assignments (4 @ 15% each)
20%: Final project
(No exams)
Frequently Asked Questions
- Q: I really want to take this course, but I am not a CSE graduate student. How do I enroll?
- A1: Advanced undergrads who have completed CSE 446 (or will complete 446 during winter quarter) are welcome to enroll in 599N, space permitting. Please request enrollment through the CSE graduate advisors. Per Allen School policy, undergraduates must wait until Period II registration (Mar 8, 2024) to enroll in graduate-level courses.
- A2: For others, please use our CSE non-major registration process.
- Q: I really want to take this course, but there's a conflict with another course I want to take. When will this course be offered next?
- A: RECENT UPDATE: This course will likely be offered again in either 25wi or 25sp.
- Q: How will this course compare to CSE 446/546?
- A1: Expect this course to be quite complementary to 446/546. We will learn mostly non-overlapping techniques, though still sticking to a largely probabilistic persepctive.
- A2: We will do quite a bit of math together during class (like in 446/546), but there will be less math on the assignments in this course.
- A3: The ML models we cover will all be motivated by exciting neuroscience applications. Each assignment will include some modeling of real neural data.
- A: Unlike 446/546, we will not have exams in this course.