ENGG 419 - Deep Learning

Description

Although there are many approaches to machine learning, deep learning neural networks have emerged as uniquely powerful tools for addressing a broad and growing range of problems, from generative language and image models, to navigating real and virtual spaces, to Nobel-winning approaches to historically difficult problems like protein folding. The purpose of this course is to equip students with the knowledge and skills to design, implement, interpret, and make smart deployment decisions about deep learning models, particularly those trained on temporally- and/or spatially-structured datasets such as obtained from sensors. This course will balance a theoretical treatment of various approaches to deep learning with practical skill-building labs and discussions with instructors and peers. The course is roughly divided into two sections – predictive and generative networks – with weekly individual labs and larger team-based projects at the end of each section.

Notes

This course is open only to students enrolled in the online MEng in Computer Engineering program. This course cannot be used to satisfy any AB, BE, MEM, MS, PhD, or residential MEng degree requirements.