CSCI 4052U: Machine Learning II
Faculty of Science, Ontario Tech University
This course is for students with a foundational understanding of machine learning. It explores advanced topics including, but not limited to, encoder/decoder architectures, attention mechanisms, and transformer-based models. The course demonstrates the application of these advanced architectures in creating state-of-the-art neural networks for various applications, such as language modeling (both masked and generative), computer vision, text-to-speech, speech recognition, multimodal learning, and Q-learning in agent-based AI systems. Additionally, it covers essential methodologies for developing and deploying AI systems, encompassing aspects like data pipelines, model management, training and fine-tuning, quantization, model distillation, knowledge infusion, and performance monitoring.
Required reading and reference material
- We will use a text, but only selected chapters.
- The text is intented to provide foundational knowledge required to understand other assigned reading, and course notes to be provided on this website.
Additional material
- Research papers will be provided on the topics we include in the curriculum.
- Students are encourage to gain the ability to extract information from research papers on the topic of deep learning.