Mathematical Tools for Neuroscience

Student-instructed. This course aims to equip biosciences graduate students with the fundamental skills in quantitative modeling and data analysis necessary for neuroscience research. It covers techniques including linear algebra, Fourier transforms, probability and statistics, signal detection, statistical inference, information theory, and introductory machine learning using deep neural networks. Students will get hands-on practice with these techniques through a coding component included in the homework assignments. This course is open to all graduate students. Undergraduates may enroll by special request. This course may be used towards the advanced neuroscience course requirement for Neurosciences IDP students.