2020–21
Applied Fundamentals of Deep Learning
APS 360 | University of Toronto
Topics to be covered will include neural networks, autoencoders/decoders, recurrent neural networks, natural language processing, and generative adversarial networks. Special attention will be paid to fairness and ethics issues surrounding machine learning. An applied approach will be taken, where students get hands-on exposure to the covered techniques through the use of state-of-the-art machine learning software frameworks.
Special Topics in Software: Empirical Methods
ECE 444 | University of Toronto
After taking this class, the student should be able to Compare the suitability of different research designs, Compare the suitability of different research designs, Combine research methods in a mixed-methods design, Mine data from online repositories, Run statistical tests and interpret results, Build, validate, and interpret regression models, Draw conclusions from empirical data and present results verbally and in writing
2019–20
ECE 444 | University of Toronto
Topics covered: Process consideration for software development, Empirical methods in software engineering, Software Engineering Research