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Jan 10, 2021Welcome to CS205L! Please sign up on Piazza for more course info and announcements.


A survey of numerical approaches to the continuous mathematics used throughout computer science with an emphasis on machine and deep learning. Although motivated from the standpoint of machine learning, the course will focus on the underlying mathematical methods including computational linear algebra and optimization, as well as special topics such as automatic differentiation via backward propagation, momentum methods from ordinary differential equations, CNNs, RNNs, etc. Written homework assignments and (straightforward) quizzes focus on various concepts; additionally, students can opt in to a series of programming assignments geared towards neural network creation, training, and inference.

This course replaces 205A and satisfies all similar requirements.

A Motivational Thought

"Everyone is sure of this [that errors are normally distributed], Mr. Lippman told me one day, since the experimentalists believe that it is a mathematical theorem, and the mathematicians that it is an experimentally determined fact."--Henri Poincaré

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