|Fall and Spring, 3 Credits, ABCF Grading
Machine Learning is centered around automated methods that improve their own performance through learning patterns in data, and then use the uncovered patterns to predict the future and make decisions. Examples include document/image/handwriting classification, spam filtering, face/speech recognition, medical decision making, robot navigation, to name a few. See this for an extended introduction. This course covers the theory and practical algorithms for machine learning from a variety of perspectives. The topics include Bayesian networks, decision tree learning, Support Vector Machines, statistical learning methods and unsupervised learning, as well as theoretical concepts such as the PAC learning framework, margin-based learning, and VC dimension. Short programming assignments include hands-on experiments with various learning algorithms, and a larger course project gives students a chance to dig into an area of their choice. See the syllabus for more. This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning.
At the end of the course, students should be able to
|Major Topics Covered in Course