Units:

12

Description:

The Advanced Machine Learning investigates the question "how can we build computer programs that automatically improve their performance through experience?" This course is designed to give students an advanced 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 with focus on the latest development in the past decade in these areas. This course is intended to give students abundant hands-on experience with different machine learning algorithms and large-scale data sets. This course assumes that students have already taken an introductory course in machine learning, and has several distinguishing features: It investigates more advanced techniques and algorithms; is more concerned with large-scale data sets; focuses on specific application areas (such as system health management, text analysis, mobile user behavior modeling, scientific data analysis, etc.); and has a focus in which recent papers are presented and discussed.

Last Modified: 2017-10-30 4:22PM

Semesters offered:

• Spring 2018
• Spring 2016
• Spring 2015
• Spring 2014