Faculty of Science
Below are the courses available from the EXSC subject code. Select a course to view the available classes, additional class notes, class times, and textbooks.
Learn Python programming as part of an introduction to machine learning (ML) applications. No previous programming experience is required, but learners must have basic computer and high-school algebra skills. The main topics are: variables, expressions, conditional execution, iteration, functions, how to edit/execute/debug python programs, testing, parsing, computational problem solving, and iterative software development. Includes hands-on, in-class exercises, students must be prepared to (in class) write Python programs, individually or in a group.
Learn intermediate Python programming and how to program a machine learning (ML) application. The class will implement a binary Naive Bayes classifier from scratch, over the course. Important ML concepts such as supervised learning, quality training data, cross validation, precision, recall, and a confusion matrix will be discussed and implemented in Python. Includes hands-on, in-class exercises, students must be prepared to (in class) write Python programs, individually or in a group. Prerequisite: EXSC 2500