Principles, Methods, and Applications of Deep Learning. Basic Artificial Neural Networks, Backpropagation and Optimization Methods, Convolutional Networks, Recurrent Networks, Principles Of Generalization and Prior Knowledge, and Principles of Architecture Design. The Course Will Mainly Focus On Supervised Learning and Will Contain a Substantial Component On Reinforcement Learning. The Course Will Briefly Mention Unsupervised Learning, Transfer Learning, Adversarial Learning, and Multi-modal Learning. We Will Learn Applications in Machine Vision, Natural Language Models, Language Translation, and Game Playing. Learning Outcomes# Upon Completion of This Course The Students Will Acquire# 1. Understanding of The Basic Tools and Methods in The Area of Deep Learning. 2. The Capability to Implement Deep Neural Networks. 3. The Capability to Understand and Implement Basic Applications In Various Areas Including Natural Language Processing, Machine Vision And Game Playing.

Faculty: Computer Science
|Undergraduate Studies |Graduate Studies
Pre-required courses

46195 - Machine Learning or 236756 - Introduction to Machine Learning

Course with no extra credit

46211 - Deep Learning