18-799/96-842 Special Topics in Signal Processing: Advanced Machine Learning
The Advanced Machine Learning Seminar investigates the question "how can we build computer programs that automatically improve their performance through experience?" This course is designed to givestudents a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The topics of the course draw from machine learning, classical statistics, data mining, Bayesian statistics and information theory and other areas. This course is project-oriented and is intended to give students abundant hands-on experience with different machine learning algorithms and large-scale data sets. Compared to introductory courses in machine learning, this course has several distinguishing features: It investigates more advanced techniques and algorithms; is more concerned with large-scale data sets; focuses on specific application areas (in particular system health management and text processing); and has a seminar focus in which recent papers are presented and discussed. There are no traditional home-works. Since the class is seminar-style, it is in part student-driven, and students are each expected to present 1-2 papers during class (depending on enrollment).
Credit units: 12
Prerequisites: Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be at an advantage. In particular, it is assumed that students have already taken an introductory machine learning course such as ECE 18-799 “Special Topics in Signal Processing: Statistical Discovery and Learning,” CS 10-701/15-781 “Machine Learning,” or a similar course. Course projects require students to have programming skills in either C/C++, Java or a similar programming languages.