Course Description: Introduction to statistical learning; Bayesian paradigm; model selection; simultaneous inference; bootstrap and cross validation; classification and clustering methods; PCA; nonparametric smoothing techniques. All rights reserved. Prerequisite(s): STA106; STA108; STA131C; STA232B; MAT167. At most, one course used in satisfaction of your minor may be applied to your major. Relation to other probability courses provided by the statistics department at Davis STA 130A: Basic probability concepts/results and estimation theory; STA 200A: More serious in the mathematics of . 3 lectures per week will be posted (except for weeks with academic holidays when only 2 lectures will be posted) Course Description: Third part of three-quarter sequence on mathematical statistics. Prerequisite: STA 131A C- or better or MAT 135A C . University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. ), Statistics: Computational Statistics Track (B.S. ), Statistics: Statistical Data Science Track (B.S. Topics selected from: martingales, Markov chains, ergodic theory. STA 290 Seminar: Sam Pimentel. If you have to take sta 131a, he's not a bad choice because he is generous with his grading scheme, which makes up for the conceptual difficulty and 4 midterms + final (a midterm is dropped). Concepts of correlation, regression, analysis of variance, nonparametrics. Summary of course contents: . Prerequisite(s): STA231B; or the equivalent of STA231B. You are encouraged to contact the Statistics Department's Undergraduate Program Coordinator [email protected] you have any questions about the statistics major tracks. (MAT 016C C- or better or MAT 017C C- or better or MAT 021C C- or better); (STA 013 C- or better or STA 013Y C- or better or STA 032 C- or better or STA 100 C- or better). -- A. J. Izenman. Course Description: Classical and Bayesian inference procedures in parametric statistical models. Illustrative reading: Course Description: Examination of a special topic in a small group setting. Analysis of incomplete tables. I've looked at my friend's 131B material and it's pretty similar, I think 131B is a little bit more theoretical than . ), Statistics: Machine Learning Track (B.S. UC Davis Department of Statistics University of California, Davis , One Shields Avenue, Davis, CA 95616 | 530-752-1011 Prentice Hall, Upper Saddle River, N.J. Instructor: Prof. Peter Hall Lecture times: 11.00 am Mondays, Wednesdays and Fridays, in Olson 223. Intensive use of computer analyses and real data sets. ), Statistics: Machine Learning Track (B.S. Prerequisite:STA 131A C- or better or MAT 135A C- or better; consent of instructor. *Choose one of MAT 108 or 127C. >> endobj Format: Lecture: 3 hours. General linear model, least squares estimates, Gauss-Markov theorem. >> endobj History: Course Description: Resampling, nonparametric and semiparametric methods, incomplete data analysis, diagnostics, multivariate and time series analysis, applied Bayesian methods, sequential analysis and quality control, categorical data analysis, spatial and image analysis, computational biology, functional data analysis, models for correlated data, learning theory. Multiple comparisons procedures. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011.
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