Special Laboratory Subject in Electrical Engineering and Computer Science
Laboratory subject that covers content not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
January IAP 2026 · Introduction to Deep Learning
This is MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Prerequisites assume calculus (i.e. taking derivatives) and linear algebra (i.e. matrix multiplication), and we'll try to explain everything else along the way! Experience in Python is helpful but not necessary.
Prerequisites & course information
Prereq: Permission of instructor U (IAP) Not offered regularly; consult department Units arranged [P/D/F] Can be repeated for credit.
Sources
- Course catalogue ↗2026–2027
- MIT OpenCourseWare ↗January IAP 2026
- MIT OCW syllabus ↗
- Historical MIT OpenCourseWare offering. Named reading entries and schedule topics are automatically extracted from the linked source pages.