Engineers encounter data in many of their tasks, whether the sources of this data may be from experiments, databases, or the Internet. There is a need for effective methods to analyze data, extract useful knowledge from it and to know how to act on it. In this course students will learn how to analyse and prepare data, describe and apply theoretical concepts in Data Science and Machine Learning, design data processing pipelines and implement important machine learning algorithms on a range of datasets and tasks. Students will gain practical experience with coding and analysis through assignments. Research students will have opportunity to connect course material to their research as a project instead of some of the assignments.

Instructor: Prof. Mark Crowley
Course Outline: https://compthinking.github.io/DKMA/outline/
Discussion Board: Piazza
Full Website: DKMA (news, assignments, readings and more)
Term: Regularly offered in Winter term. Has sometimes been offered in Spring as overflow. Recent offerings (S21, W21, W20, W19, W18)
Office: ECE 4114
Twitter: @compthink