Introduction to Semi-Supervised Learning

Xiaojin Zhu is an assistant professor in the Computer Sciences department at the University of Wisconsin-Madison. His research interests include statistical machine learning and its applications in cognitive psychology, natural language processing, and programming languages. Xiaojin received his Ph.D. from the Language Technologies Institute at Carnegie Mellon University in 2005. He worked on Mandarin speech recognition as a research staff member at IBM China Research Laboratory in 1996-1998. He received M.S. and B.S. in computer science from Shanghai Jiao Tong University in 1996 and 1993, respectively. His other interests include astronomy and geology. Andrew B.Goldberg is a Ph.D. candidate in the Computer Sciences department at the University of Wisconsin-Madison. His research interests lie in statistical machine learning (in particular, semi-supervised learning) and natural language processing. He has served on the program committee for national and international conferences including AAAI, ACL, EMNLP, and NAACL-HLT. Andrew was the recipient of a UW-Madison First-Year Graduate School Fellowship for 2005-2006 and a Yahoo! Key Technical Challenges Grant for 2008-2009. Before his graduate studies, Andrew received a B.A. in computer science from Amherst College, where he graduated magna cum laude with departmental distinction in 2003. He then spent two years writing, editing, and developing teaching materials for introductory computer science and Web programming textbooks at Deitel and Associates. During this time, he contributed to several Deitel books and co-authored the 3rd edition of Internet & World Wide Web How to Program. In 2005, Andrew entered graduate school at UW-Madison and, in 2006 received his M.S. in computer science. In his free time, Andrew enjoys live music, cooking, photography, and travel.

• Introduction to Statistical Machine Learning.- Overview of Semi-Supervised Learning.- Mixture Models and EM.- Co-Training.- Graph-Based Semi-Supervised Learning.- Semi-Supervised Support Vector Machines.- Human Semi-Supervised Learning.- Theory and Outlook.