CPSC/AMTH 663 - Deep Learning Theory and Applications - Spring 2018 Yale
Yale University CPSC/AMTH 663 - S2018


Deep Learning Theory and Applications

Spring 2018

Instructors: Kevin Moon (kevin.moon@yale.edu) & Guy Wolf (guy.wolf@yale.edu)

TA: Emily Guo (xingwen.guo@yale.edu)
ULAs: Tyler Dohrn (tyler.dohrn@yale.edu), Scott Stankey (scott.stankey@yale.edu), & Alex Atanasov (alexander.atanasov@yale.edu)

Deep neural networks have gained immense popularity within the last decade due to their outstanding success in many important machine learning tasks such as image recognition, speech recognition, and natural language processing. This course will provide a principled and hands-on approach to deep learning with neural networks. By the end of the course, students will have mastered the principles and practices underlying neural networks including modern methods of deep learning, and will have applied deep learning methods to real-world problems including image recognition, natural language processing, and biomedical applications.

The course will be based on homework and a final group project. The project will include both a written and oral (i.e. presentation) component. Grades in this courser will be based on their homework scores and the quality of the written and oral component of their projects. The course assumes basic prior knowledge in linear algebra and probability.



Tuesdays & Thursdays 4:00-5:15, DL 220
First lecture on Tuesday, January 23rd


Python for Machine Learning: Monday, Feb. 12th, 6:00-8:00 PM, AKW 400
TensorFlow: Monday, Feb. 26th, 7:00-8:00 PM, AKW 400

Office Hours:

ULAs: Mondays 6:00-8:00 PM, AKW 400 (or by appointment)
TA: Tuesdays 5:30-7:30 PM, AKW 104 (or by appointment)
Kevin Moon: Wednesdays 3:00-5:00 PM, AKW 103
Guy Wolf: Thursdays 5:30-7:30 PM in AKW 103



This is a tentative list of topics we intend to cover, which may change as we progress through the course:


Next topics to be uploaded after they are presented in class (subject to changes):