Machine Learning: Unsupervised Learning

Overview

The "Machine Learning: Unsupervised Learning" course, part of a three-course ensemble at Georgia Tech identified as CS7641, explores the intriguing world of unsupervised learning without offering Georgia Tech credit for participants.

This intermediate course demystifies how platforms like Netflix predict your movie preferences or Amazon foresees your shopping desires using unsupervised learning. A cornerstone of data analysis and pattern recognition, unsupervised learning enables the discovery of inherent structures within unlabeled data. Learners will delve into techniques such as randomized optimization, clustering, and feature selection and transformation to uncover these patterns.

This installment, offered in collaboration with the Indian Institute of Technology, Kharagpur and provided by Udacity, continues the exploration of Artificial Intelligence within the Machine Learning graduate series. Guided by a conversational approach between Professors Charles Isbell of Georgia Tech and Michael Littman of Brown University, the course offers a unique and engaging educational experience focusing on practical applications of machine learning.

Filed under both Machine Learning and Unsupervised Learning categories, this course is a must for those keen on mastering the art of analyzing and leveraging data structures through advanced AI techniques.

Syllabus

Taught by

Charles Isbell and Michael Littman

Tags

united states