Deep learning is the study of how computers can learn from data in a manner inspired by neural connections in the human brain. It is revolutionizing how people and machines interact. This course explores the principles and practice of modern deep learning systems. Students will design and implement their own artificial neural networks as well as analyze massive deep learning models at the forefront of the field of machine learning. Deep learning algorithms such as convolutional neural networks and recurrent neural networks will be applied in a variety of domains, including medical diagnosis, self-driving cars, and large-language models. Students will further investigate the societal impacts and ethical considerations of these deep learning systems.

Units: 1

Max Enrollment: 18

Prerequisites: CS 230 and MATH 225.

Instructor: Tjaden

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Spring

Semesters Offered this Academic Year: Spring

Notes: