|Offered this year?||Yes|
|When Offered?||Every year|
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.
Registration RestrictionsThis course is only open to students in the DSI Master's program.