Deep Learning

Not offered this year
Offered every year, last taught:

Spring 2022

A hands-on introduction to neural networks, reinforcement learning, and related topics. Students will learn the theory of neural networks, including common optimization methods, activation and loss functions, regularization methods, and architectures. Topics include model interpretability, connections to other machine learning models, and computational considerations. Students will analyze a variety of real-world problems and data types, including image and natural language data.

Prerequisites: DATA 1030 and 1050.

Enrollment limited to students in the Data Science (SCM) program.

  • Andras Zsom