Computer Science 390 - UNSUPERVISED MACHINE LEARNING

This course begins with a brief history of artificial intelligence (AI) and a roadmap of how the material in this course fits into the overall field of AI. During the first few weeks we cover some classical AI material such as rule-based expert systems. Then we move on to a discussion of supervised vs. unsupervised machine learning, focusing on the latter. Unsupervised learning seeks to uncover underlying structure in a dataset or system, without the use of labeled data. We explore unsupervised learning methods from a variety of angles, including theory, implementation, application, existing software and recent literature. Throughout the course we investigate a variety of datasets, with an emphasis on “big data” (i.e., natural language and biological datasets).

Instructor Permission. Not open to first-years, sophomores