General Information



Machine learning (ML) is the scientific study of algorithms and statistical models that computers use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. This course will cover advanced topics in machine learning such as deep learning including CNNs, RNNs, GANs, Transformers, Deep clustering, transfer learning, domain adaptation, few-shot learning, zero-shot learning, self-supervised learning and Interpretability of ML methods. The format of the course will be a mix of lectures and paper presentations.

Course Information


You are expected to have a reasonable background in machine learning and be familiar with probability, statistics, linear algebra, calculus and Python.


Course Materials

There is no required text for this course. Notes will be posted periodically in the course schedule.

Paper presentations



Academic Accommodation

You may need special arrangements to meet your academic obligations during the term. For an accommodation request, see here for more information.

Student Academic Integrity Policy

Every student should be familiar with the Carleton University student academic integrity policy described here.