Spring 2021
INF 385T Special Topics in Information Science: Introduction to Machine Learning
Synchronous online class meetings with additional asynchronous online coursework to be arranged. NOTE: In addition to the Monday afternoon lecture meeting, an additional mandatory online lab meeting will take place on Tuesdays 4-5pm.
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.
Prior programming experience is strongly recommended.
RESTRICTIONS
Restricted to graduate students in the School of Information.