STATS 200: Introduction to Statistical Inference
Modern statistical concepts and procedures derived from a mathematical framework. Statistical inference, decision theory; point and interval estimation, tests of hypotheses; NeymanPearson theory. Bayesian analysis; maximum likelihood, large sample theory. Prerequisite: 116.
http://statweb.stanford.edu/~sabatti/Stat200/index.html
Terms: Aut, Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Romano, J. (PI)
;
Bhattacharya, S. (TA)
;
Ghosh, S. (TA)
...
more instructors for STATS 200 »
Instructors:
Romano, J. (PI)
;
Bhattacharya, S. (TA)
;
Ghosh, S. (TA)
;
Gupta, S. (TA)
;
Hamidi, N. (TA)
;
Hwang, J. (TA)
;
Roquero Gimenez, J. (TA)
STATS 202: Data Mining and Analysis
Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees, association rules, clustering, case based methods, and data visualization. Prereqs: Introductory courses in statistics or probability (e.g.,
Stats 60), linear algebra (e.g.,
Math 51), and computer programming (e.g.,
CS 105).
Terms: Aut, Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Patel, R. (PI)
;
Walther, G. (PI)
;
Feldman, M. (TA)
...
more instructors for STATS 202 »
Instructors:
Patel, R. (PI)
;
Walther, G. (PI)
;
Feldman, M. (TA)
;
Markovic, J. (TA)
;
Orenstein, P. (TA)
;
Qian, J. (TA)
;
Ruan, F. (TA)
;
Tsao, A. (TA)
;
Tuzhilina, E. (TA)
;
Zhang, Y. (TA)
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