EC ENGR C143A
Neural Signal Processing
Description: Lecture, four hours; discussion, one hour; outside study, seven hours. Requisites: course 131A, Mathematics 33A. Topics include fundamental properties of electrical activity in neurons; technology for measuring neural activity; spiking statistics and Poisson processes; generative models and classification; regression and Kalman filtering; principal components analysis, factor analysis, and expectation maximization. Concurrently scheduled with course C243A. Letter grading.
Units: 4.0
Units: 4.0
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Most Helpful Review
Spring 2020 - This is the third class I've taken with Professor Kao, having taken his Systems and Signals, and Neural Networks and Deep Learning classes. As always, Kao's lectures are very clear, informative and interesting. The HW can be a bit tricky- start ahead of time and go to office hours. Throughout the quarter, Kao was very accommodating and even relaxed the grading scales at the end of quarter. Due to COVID-19 and remote learning, we weren't able to cover the amount of material the class usually covers. However, for context: the first third of the class covers basic neuroscience, including action potentials and how the brain works. The second third covers Poisson processes and discrete classification. The third half covers decoding including Wiener and Kalman filters, which I think is the most interesting part of the course. If you are looking for an interesting and useful elective, this is the class for you. Highly recommended- 10/10.
Spring 2020 - This is the third class I've taken with Professor Kao, having taken his Systems and Signals, and Neural Networks and Deep Learning classes. As always, Kao's lectures are very clear, informative and interesting. The HW can be a bit tricky- start ahead of time and go to office hours. Throughout the quarter, Kao was very accommodating and even relaxed the grading scales at the end of quarter. Due to COVID-19 and remote learning, we weren't able to cover the amount of material the class usually covers. However, for context: the first third of the class covers basic neuroscience, including action potentials and how the brain works. The second third covers Poisson processes and discrete classification. The third half covers decoding including Wiener and Kalman filters, which I think is the most interesting part of the course. If you are looking for an interesting and useful elective, this is the class for you. Highly recommended- 10/10.
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