{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

stat_231_syllabus - Stat 231 Pattern Recognition and...

Info icon This preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon
MW 9:30-10:50 Am, Fall 2010, Math Science 5128 www.stat.ucla.edu/~sczhu/Courses/UCLA/Stat_231/Stat_231.html Course Description This course introduces fundamental concepts, theories, and algorithms for pattern recognition and machine learning, which are used in computer vision, speech recognition, data mining, statistics, information retrieval, and bioinformatics. Topics include: Bayesian decision theory, parametric and non-parametric learning, data clustering, component analysis, boosting techniques, kernel methods and support vector machine, and fast nearest neighbor indexing and hashing. Prerequisites Math 33A Linear Algebra and Its Applications, Matrix Analysis Stat 100B Intro to Mathematical Statistics, CS 180 Intro to Algorithms and Complexity. Textbook R. Duda, P. Hart, D. Stork, " Pattern Classification ", second edition, 2000. [Required] [ link to book page ] C.M. Bishop, " Pattern Recognition and Machine Learning ", Springer, 2006 [Reference] T. Hastie, R. Tibshurani, and J.H. Friedman, " The Elements of Statistical Learning: Data Mining, Inference, and Prediction ", Spinger Series in Statistics, 2001. [Reference] N. Cristianini and J. Shawe-Taylor, "
Image of page 1

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 2
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern