Catalog Description
Cutting edge concepts employed in machine learning to solve artificial intelligence problems. Students will learn the theory behind a range of machine learning tools and practice applying the tools to, for example, textual data (natural language processing), visual data (computer vision), and the combination of both textual and visual data.
Instructor Description
This course will cover fundamental concepts in Machine Learning (ML). The course will provide conceptual and practical knowledge on a wide range of modern machine learning algorithms; including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), reinforcement learning & deep learning models (CNN, RNN, Autoencoders & Transformers) and also introduce the importance of Prompt Engineering and Retrieval Augmented Generation. The goal is for students to be comfortable and confident in machine learning concepts and have the ablity to build machine learning model solution to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, this is a great place to start.
Prerequisites
Graduate standing.
Course Areas
Data Science/Engineering/Analytics