EC ENGR C243A
Neural Signal Processing and Machine Learning
Description: Lecture, four hours; discussion, one hour; outside study, seven hours. Requisite: 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 C143A. Letter grading.
Units: 4.0
Units: 4.0
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Most Helpful Review
Spring 2023 - A great course with a great lecturer and TAs. The lectures are well prepared and Prof. Kao is really good at teaching. He's happy to stop anytime and answer your questions. TAs are very helpful in the discussions and OH. The exams are fair, and do please attend the midterm and final review held by the TA! The topics are very similar to what will appear in the exam so you definitely should spend enough time reviewing these topics. If you are interested in the neuroscience and have a strong knowledge base of probability, linear algebra and Python, the course is a perfect choice. A little bit of matrix calculus is involved but truse me, they just look scary. 40% 6 homeworks, 25% midterm, 35% final.
Spring 2023 - A great course with a great lecturer and TAs. The lectures are well prepared and Prof. Kao is really good at teaching. He's happy to stop anytime and answer your questions. TAs are very helpful in the discussions and OH. The exams are fair, and do please attend the midterm and final review held by the TA! The topics are very similar to what will appear in the exam so you definitely should spend enough time reviewing these topics. If you are interested in the neuroscience and have a strong knowledge base of probability, linear algebra and Python, the course is a perfect choice. A little bit of matrix calculus is involved but truse me, they just look scary. 40% 6 homeworks, 25% midterm, 35% final.
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