Hsuan-Tien Lin




Prof. Hsuan-Tien Lin received a B.S. in Computer Science and Information Engineering from National Taiwan University in 2001, an M.S. and a Ph.D. in Computer Science from California Institute of Technology in 2005 and 2008, respectively. He joined the Department of Computer Science and Information Engineering at National Taiwan University as an assistant professor in 2008, and has been an associate professor since August 2012.

Prof. Lin received the Distinguished Teaching Award from the university in 2011, and the Outstanding Mentoring Award from the university in 2013. He co-authored the introductory machine learning textbook Learning from Data. His research interests include theoretical foundations of machine learning, studies on new learning problems, and improvements on learning algorithms. He received the 2012 K.-T. Li Young Researcher Award from the ACM Taipei Chapter, and the 2013 D.-Y. Wu Memorial Award from National Science Council of Taiwan. He co-led the teams that won the third place of KDDCup 2009 slow track, the champion of KDDCup 2010, the double-champion of the two tracks in KDDCup 2011, the champion of track 2 in KDDCup 2012, and the double-champion of the two tracks in KDDCup 2013. He is currently serving as the Secretary General of Taiwanese Association for Artificial Intelligence.

More info: http://www.csie.ntu.edu.tw/~htlin/

Customize your search:

E.g., 2016-10-27
E.g., 2016-10-27
E.g., 2016-10-27
Nov 10th 2015

The course extends the fundamental tools in "Machine Learning Foundations" to powerful and practical models by three directions, which includes embedding numerous features, combining predictive features, and distilling hidden features. [這門課將先前「機器學習基石」課程中所學的基礎工具往三個方向延伸為強大而實用的工具。這三個方向包括嵌入大量的特徵、融合預測性的特徵、與萃取潛藏的特徵。]

Average: 4 (1 vote)
Sep 8th 2015

Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. The course teaches the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know.

Average: 9.5 (2 votes)