ESE 5460 Principles of Deep Learning
Short Description
The purpose of this course is to deconstruct the hype by teaching deep learning theories, models, skills, and applications that are useful for applications. Here, we will shed light on the methods behind the magic of Deep Learning. But we don't stop there: We further look into the societal implications of deep learning and how we can design more ethical algorithms.
Portfolio Building Course
No
Pre-Requisites
CIT 5910, CIT 5920, CIS 5150 (ESE 5410 or CIT 5210 recommended, can be taken concurrently)
Content
Deep networks are at the heart of modern approaches in computer vision, natural language processing and robotics. Design of these networks requires a combination of intuition, theoretical foundation and empirical experience; this course discusses general principles of deep learning that cut across these three. It develops insight into popular empirical practices with a focus on the training of deep networks, builds theoretical skills to develop new ideas in deep learning and to deploy deep networks in real world applications. A fair degree of mathematical and programming proficiency is necessary to complete the coursework.