Computer Science 281A

[Email HKN about this data] [Info about this page]

Title	Statistical Learning Theory
Units	3
Prerequisites	Linear algebra, calculus, basic probability, and statistics, algorithms. Recommended 289.
Description	Classification regression, clustering, dimensionality, reduction, and density estimation. Mixture models, hierarchical models, factorial models, hidden Markov, and state space models, Markov properties, and recursive algorithms for general probabilistic inference nonparametric methods including decision trees, kernal methods, neural networks, and wavelets. Ensemble methods. Also listed as Statistics C241A.

Sections Instructor Teaching Effectiveness How worthwhile was this course?

Fall 2007 Jordan 6.1 / 7 ± 0.2
6.2 / 7 ± 0.3

Fall 2005 Wainwright 6.0 / 7 ± 0.3
5.8 / 7 ± 0.4

Fall 2004 Jordan 6.1 / 7 ± 0.2
6.0 / 7 ± 0.3

Fall 2003 Bartlett 5.4 / 7 ± 0.5
6.0 / 7 ± 0.6

Fall 2002 Jordan 6.0 / 7 ± 0.3
6.1 / 7 ± 0.4

Overall Rating Teaching Effectiveness How worthwhile was this course?

Computer Science 281A 5.9 / 7 ± 0.1
6.0 / 7 ± 0.1

Hint: You can click on the colored rating bars to see detailed statistics on a particular rating.