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- Free video course on Machine Learning by Andrew Ng of Stanford. Note: This course is offered by Stanford as an <a href="http://scpd.stanford.edu/search/pub...Free video course on Machine Learning by Andrew Ng of Stanford. Note: This course is offered by Stanford as an <a href="http://scpd.stanford.edu/search/publicCourseSearchDetails.do?method=load&courseId=11763" target="_blank" onClick="javascript: pageTracker._trackPageview('/outgoing/SCPD_CS229');">online course for credit</a>. It can be taken individually, or as part of a <a href="http://scpd.stanford.edu/certificates/mastersDegrees.jsp" target="_blank" onClick="javascript: pageTracker._trackPageview('/outgoing/SCPD_Masters');">master’s degree</a> or <a href="http://scpd.stanford.edu/certificates/graduateCertificates.jsp" target="_blank" onClick="javascript: pageTracker._trackPageview('/outgoing/SCPD_Professional_Certificate');">graduate certificate</a> earned online through the <a href="http://scpd.stanford.edu/" target="_blank" onClick="javascript: pageTracker._trackPageview('/outgoing/SCPD_Home');">Stanford Center for Professional Development</a>. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. Prerequisites: Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program; familiarity with basic probability theory; familiarity with basic linear algebra.
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- High-Performance Computing and Networking, volume 2110 of Lecture Notes in Computer Science, Springer, Berlin / Heidelberg, (2001)
- volume 497 of CEUR-WS.org, (September 2009)
- HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia, New York, NY, USA, ACM, (July 2009)
- IBM Journal of Research and Development 2(2):159--165 (1958)
- HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia, page 157--166. New York, NY, USA, ACM, (2008)
- Arxiv preprint arXiv:0904.1113 (2009)
- W3C - World Wide Web Consortium, (January 2004)
- ICSE, page 12-22. IEEE, (2009)
- (2008)
- Proceedings of the 1st Workshop on Explorative Analytics of Information Networks EIN2009, Bled, Slovenia, (September 2009)
- page 50-57. (August 2008)
- Information Technology: New Generations, Sixth International Conference on (2009)
- SPIRE, volume 5280 of Lecture Notes in Computer Science, page 188-199. Springer, (2008)
- SIGMOD Rec. 28(1):54--59 (1999)
- AI*IA 2003: Advances in Artificial Intelligence (2003)
- KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, page 76--85. New York, NY, USA, ACM, (2007)description = Extracting semantic relations from query logs, location = San Jose, California, USA, isbn = 978-1-59593-609-7, doi = http://doi.acm.org/...description = Extracting semantic relations from query logs, location = San Jose, California, USA, isbn = 978-1-59593-609-7, doi = http://doi.acm.org/10.1145/1281192.1281204.
- 6th Annual European Semantic Web Conference ESWC2009, page 669-683. (June 2009)
- WSCD '09: Proceedings of the 2009 workshop on Web Search Click Data, page 8--14. New York, NY, USA, ACM, (2009)
- Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008, 4956, page 101-113. Springer, (2008)
- SCAM '07: Proceedings of the Seventh IEEE International Working Conference on Source Code Analysis and Manipulation, page 193--202. Washington, DC, USA, IEEE Computer Society, (2007)
