COMP642: Machine Learning


Description: This class aims to train future professional industry leaders in machine learning. The course will focus on the foundation and practice of widely adopted modern ideas and principles that make a difference in real applications.

Redesigned Topics: We are well aware that many well-established ideas in traditional machine learning are either becoming obsolete or are getting questioned under the light of more observations and experimentations with deep learning. This course is redesigned to eliminate them. Most existing mathematics fails to explain the success of deep and machine learning. Also, most successful models such as transformers, etc., came independent of the rigorous understanding of deep learning. A primary aim of this course is to develop an instinct for practical machine learning via case studies and assignments, which is one of the essential skills for success in the field.

Coverage: We will cover all aspects of modern machine learning (See schedule below), including Deep Learning Architectures, Graph Neural Networks, Self Supervised Learning, Tiny ML, Distributed and Federated ML, etc. The course will also demonstrate that while machine learning seems to have too many topics, the motivating fundamentals are only a handful.

Prerequisite: A prior coursework on machine learning is preferred but not required. It turns out that the most sophisticated machine learning systems and algorithms of today do not require significant mathematical preparations. Basic probability and multivariate calculus, along with Linear algebra at the vector spaces and matrix manipulations, are sufficient mathematical foundations for this course. The course does require rigorous experience in programming. Design and analysis of algorithms and basic data structures to understand compute and memory efficiency. Basic knowledge of computer systems such as cache hierarchy, memory latency will be required to understand the practicality of ideas used in modern ML systems.

If you are unsure about your prerequisites, please contact the instructor.

Materials: Machine Learning is moving very fast. Techniques published a few years back are getting obsolete and so are textbooks and most known courses. There are no single textbooks for the materials covered in this class, some of the materials are ideas that appeared last year (Such as MLP Mixer). Lecture recordings and references will be provided during the class and will be available on the course page. Having said that, any good tutorial on deep learning with Python will be helpful for exercises.



Students with Disability

If you have a documented disability that may affect academic performance, you should: 1) make sure this documentation is on file with Disability Support Services (Allen Center, Room 111 / / x5841) to determine the accommodations you need; and 2) meet with me to discuss your accommodation needs.