Class Detail

Provides a practical and project-oriented introduction to machine learning, with an emphasis on neural networks and deep learning. Starts with a discussion of the foundational pieces of statistical inference, then introduces the basic elements of machine learning: loss functions and gradient descent. Using these, presents logistic regression, or one-layer networks, and then moves on to more complex models: deep neural networks, convolutional networks for image recognition, and recurrent networks and LSTM for temporal and sequence data. Also covers the basics of dataset preparation and visualization and the performance characterization of the models created. Includes weekly homework and a final project that can be done in groups. (Formerly CMPS 144.)

Prerequisite(s): CSE 101. Enrollment is restricted to juniors and seniors.

Prerequisites will not be waived for CSE courses. There will be no exceptions. If you have completed the prerequisites but cannot enroll contact BSOE Advising - advising@soe.ucsc.edu. Do not contact instructors, they do not have permission codes. This course is intended for UCSC students. It is not appropriate for visiting and high school students.

Days & Times	Room	Instructor	Meeting Dates
	Remote Instruction	Norouzi,N. Zare,S. Briden,M.J.	06/21/21 - 07/23/21