CS 229: Machine Learning
Machine learning is the techniques that enable computers to learn and adjust to new data without being programmed. Machine learning detects patterns in data and adjusts program action accordingly. This course is advanced and is heavy in mathematics, developed for students who want to have a strong foundation in machine learning and its techniques. As well as a sound theoretical basis, it provides practical application of machine learning needed for a successful career in data science. The course covers supervised and unsupervised classification, regression and outliers, validation and evaluation, feature scaling, instance-based learning, neural networks, kernel machines, and additive models. By the end of this course students will know how to extract and identify patterns that best represents the data at hand. The course is designed to prepare students for original research in machine learning and its associated fields.
About the Instructor
Christian Shelton received his Ph.D. from MIT in 2001. He then spent two years at Stanford as a post-doctoral scholar, followed by six months at Intel Research as a visiting faculty member. He joined the faculty at UC Riverside in 2003. He was the Managing Editor of the Journal of Machine Learning Research (JMLR) from 2003 through 2008 and a member of the Editorial Board of the Journal of Artificial Intelligence Research (JAIR) from 2009 through 2012.
Dr. Shelton's research interest is in statistical approaches to artificial intelligence, mainly in the areas of machine learning and dynamic processes. He also works at the intersection of learning and topics as varied as computer vision, sociology, game theory, control theory, and computational biology.