Description
Machine learning is an exciting interdisciplinary field, with historical roots in computer science,
statistics, pattern recognition, and even neuroscience and physics. In the past decade, many of these approaches have converged and led to rapid theoretical advances and real-world applications. This course will focus on the machine learning methods that have proven valuable and successful in practical applications. This course will contrast the various methods, with the aim of explaining the circumstances under which each is most appropriate. We will also discuss basic issues that confront any machine learning method.