05-834 Applied Machine Learning
Machine Learning is concerned with computer programs that enable the behavior of a computer to be learned from examples or experience rather than dictated through rules written by hand. It has practical value in many application areas of computer science such as online communities and digital libraries.
This class is meant to teach the practical side of machine learning for applications, such as mining newsgroup data or building adaptive user interfaces. Rather than emphasizing the theory behind what makes machine learning work, the focus will be on learning the process of effectively applying machine learning to a variety of problems.
The course does not assume prior exposure to machine learning theory or practice. The first two-thirds of the course will cover a wide range of learning algorithms that can be applied to a variety of problems. In particular, topics such as decision trees, rule based classification, support vector machines, Bayesian networks, and clustering will be covered. In the final one-third of the course, greater depth in one application area, namely the application of machine learning to problems involving text processing, such as information retrieval or text categorization, will be explored.
Prerequisites: Java programming experience is desirable, but not necessary.
Units: 12 units
Schedule: Fall (on-campus) / Spring (on-line)
Note: crosslisted as Applied Machine Learning (05-434/11-344)