Adv Topics of Machine Learning

Catalog description

This course is a graduate seminar for PhD students or Masters’ students taking the Thesis option. Students must be well motivated and have a strong interest in research in the area of machine learning. Each time this course is offered, a specific topic in the state-of-the-art in the machine learning literature is identified and the whole semester is dedicated to the state-of-the-art literature on this topic. The instructor is responsible for identifying a topic every time the course is offered, and for also identifying representative papers from the literature on this topic that will be assigned to students for their reading. Students are expected to read the assigned papers to get familiar with the state-of-the-art literature on this topic and to present the work in the papers in class. Except for the first few classes, when the instructor gives an introduction to the literature on this topic, every class consists of a student presentation and follow-up brain-storming discussions. These discussions are expected to generate productive ideas leading to new research projects. Each student shall be given a research project that the student needs to turn in either as a research paper or as a project demo at the end of the semester. Prerequisite: CS535 Intro to Data Mining or CS536 Intro to Machine Learning or an equivalent course taken at other institutions with at least a grade of B+. Offered once a year.