Course description An introduction to machine learning engineering, with a focus on neural networks. The entire process of developing a machine learning solution, from data collection to software development, as well as ethics in machine learning, will be discussed. Practical techniques in machine learning will be covered, including data augmentation and the use of pre-trained networks. Topics covered will include the fundamentals of neural networks, convolutional neural networks, recurrent neural networks, generative adversarial networks and transformer networks. Students will complete a major hands-on project in machine learning.
The course syllabus is available here.
Previous year's project instructions (subject to change)
Mini-Project 1, due Feb. 4
Mini-Project 2, due
March 28 Apr. 4
Mini-Project 3, due April 14
An inclusive environment
We strive to build and maintain an inclusive environment in class — an environment that allows every student to reach their full potential. Please do not hesitate to contact me and/or your preceptor to let us know if you need special accommodation or with any concerns.
Design credit: CS229, Jan 2019.