Deep Learning (Fall 2026)
Time and Location
- Tuesday, Thursday 1:00-2:20 PM Eastern Time
- Instructor: Chen Sun (chen_sun4@brown.edu)
- Classroom: Friedman Hall 202
In-class participation is required unless otherwise approved by the instructor.
Lectures will be recorded for asynchronous viewing.
About
Welcome to CSCI 2470! Deep Learning is a subset of machine learning methods based on artificial neural networks. It emphasizes learning representation with multiple layers (hence “deep”) of neural networks, and can be flexibly applied to diverse domains of applications, ranging from object detection, machine translation, video generation, to protein structure prediction. Our course aims to offer students the mathematical foundation and engineering skills necessary to understand, utilize, and design state-of-the-art deep learning frameworks. The field of Deep Learning research and applications is progressing at a lightning speed. In response, our course is organized with three themes: the underlying design principles shared by most modern deep learning networks, the high-level learning paradigms popularized by the success of deep learning, and several canonical neural network architectures that advance our understanding on what can be achieved by neural networks. We expect students to have taken an introductory course on machine learning, and feel comfortable with Python and object oriented programming.
See the About page for the full course policies, learning goals, grading, and GenAI usage guidelines.