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Current Courses

Title Instructor(s) Quarter Day, Time, Location
Mathematics in the Real World (MATH 16)
STATS 90 (section 1)

Introduction to non-calculus applications of mathematical ideas and principles in real-world problems. Topics include probability and counting, basic statistical concepts...

Poulson, J. 2014-2015 Spring
Monday Wednesday Friday
9:00am - 9:50am
380-380W
Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 60)
STATS 160 (section 1)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of...

Bacallado, S., Mukherjee, R. 2014-2015 Spring
Monday Tuesday Wednesday Thursday Friday
10:00am - 10:50am
420-040
Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 60)
STATS 160 (section 1)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of...

Bacallado, S., Mukherjee, R. 2014-2015 Spring
Monday Tuesday Wednesday Thursday Friday
10:00am - 10:50am
420-040
Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 60)
STATS 160 (section 2)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of...

Bacallado, S., Mukherjee, R. 2014-2015 Spring
Monday Tuesday Wednesday Thursday Friday
9:00am - 9:50am
420-040
Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 60)
STATS 160 (section 2)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of...

Bacallado, S., Mukherjee, R. 2014-2015 Spring
Monday Tuesday Wednesday Thursday Friday
9:00am - 9:50am
420-040
Introduction to R (CME 195)
STATS 195 (section 1)

This short course runs for the first four weeks of the quarter and is offered in fall and spring. It is recommended for students who want to use R in statistics, science...

Suo, X. 2014-2015 Spring
Monday Wednesday
4:30pm - 5:45pm
Hewlett Teaching Center 103
Introduction to Regression Models and Analysis of Variance
STATS 203 (section 1)

Modeling and interpretation of observational and experimental data using linear and nonlinear regression methods. Model building and selection methods. Multivariable...

Kaluwa Devage, P. 2014-2015 Spring
Tuesday Thursday
10:15am - 11:30am
380-380F
Introduction to Regression Models and Analysis of Variance
STATS 203 (section 1)

Modeling and interpretation of observational and experimental data using linear and nonlinear regression methods. Model building and selection methods. Multivariable...

Kaluwa Devage, P. 2014-2015 Spring
Tuesday Thursday
10:15am - 11:30am
380-380F
Introduction to Time Series Analysis
STATS 207 (section 1)

Time series models used in economics and engineering. Trend fitting, autoregressive and moving average models and spectral analysis, Kalman filtering, and state-space...

Donoho, D. 2014-2015 Spring
Tuesday Thursday
2:15pm - 3:30pm
380-380F
Introduction to the Bootstrap
STATS 208 (section 1)

The bootstrap is a computer-based method for assigning measures of accuracy to statistical estimates. By substituting computation in place of mathematical formulas, it...

Donoho, D. 2014-2015 Spring
Tuesday Thursday
9:00am - 10:15am
50-52H
Introduction to Stochastic Processes
STATS 218 (section 1)

Renewal theory, Brownian motion, Gaussian processes, second order processes, martingales.

Romano, J. 2014-2015 Spring
Tuesday Thursday
9:30am - 10:45am
320-220
Mathematical and Computational Finance Seminar (CME 242)
STATS 239 (section 1)
Jain, K. 2014-2015 Spring
Wednesday
4:15pm - 5:30pm
200-303
Workshop in Biostatistics (HRP 260C)
STATS 260C (section 1)

Applications of statistical techniques to current problems in medical science. To receive credit for one or two units, a student must attend every workshop. To receive two...

Olshen, R., Sabatti, C. 2014-2015 Spring
Thursday
1:15pm - 3:05pm
MSOBX303
Intermediate Biostatistics: Regression, Prediction, Survival Analysis (HRP 262)
STATS 262 (section 1)

Methods for analyzing longitudinal data. Topics include Kaplan-Meier methods, Cox regression, hazard ratios, time-dependent variables, longitudinal data structures,...

Sainani, K. 2014-2015 Spring
Monday
3:00pm - 4:30pm
Li Ka Shing Center, room 120
Intermediate Biostatistics: Regression, Prediction, Survival Analysis (HRP 262)
STATS 262 (section 2)

Methods for analyzing longitudinal data. Topics include Kaplan-Meier methods, Cox regression, hazard ratios, time-dependent variables, longitudinal data structures,...

Sainani, K. 2014-2015 Spring
Wednesday
3:15pm - 4:45pm
Li Ka Shing Center, room 120
A Course in Bayesian Statistics (STATS 370)
STATS 270 (section 1)

Advanced-level Bayesian statistics. Topics: Discussion of the mathematical and theoretical foundation for Bayesian inferential procedures. Examination of the construction...

Wong, W. 2014-2015 Spring
Monday Wednesday
9:30am - 10:45am
200-107
Theory of Statistics
STATS 300C (section 1)

Decision theory formulation of statistical problems. Minimax, admissible procedures. Complete class theorems ("all" minimax or admissible procedures are "Bayes"), Bayes...

Candes, E. 2014-2015 Spring
Monday Wednesday Friday
11:00am - 11:50am
Sequoia Hall 200
PhD First Year Student Workshop
STATS 303 (section 1)

For Statistics First Year PhD students only. Discussion of relevant topics in first year student courses, consultation with PhD advisor.

Holmes, S., Walther, G. 2014-2015 Spring
Tuesday Wednesday Thursday
12:00pm - 12:50pm
Sequoia Hall 200
PhD First Year Student Workshop
STATS 303 (section 1)

For Statistics First Year PhD students only. Discussion of relevant topics in first year student courses, consultation with PhD advisor.

Holmes, S., Walther, G. 2014-2015 Spring
Tuesday Wednesday Thursday
12:00pm - 12:50pm
Sequoia Hall 200
Methods for Applied Statistics: Unsupervised Learning
STATS 306B (section 1)

Unsupervised learning techniques in statistics, machine learning, and data mining.

Tibshirani, R. 2014-2015 Spring
Monday Wednesday
3:15pm - 4:30pm
200-002
Theory of Probability (MATH 230C)
STATS 310C (section 1)

Continuous time stochastic processes: martingales, Brownian motion, stationary independent increments, Markov jump processes and Gaussian processes. Invariance principle,...

Chatterjee, S. 2014-2015 Spring
Tuesday Thursday
9:30am - 10:45am
Sequoia Hall 200
Modern Applied Statistics: Data Mining
STATS 315B (section 1)

Two-part sequence. New techniques for predictive and descriptive learning using ideas that bridge gaps among statistics, computer science, and artificial intelligence....

Friedman, J. 2014-2015 Spring
Tuesday Thursday
2:15pm - 3:30pm
Gates B1
Stochastic Processes
STATS 317 (section 1)

Semimartingales, stochastic integration, Ito's formula, Girsanov's theorem. Gaussian and related processes. Stationary/isotropic processes. Integral geometry and geometric...

Taylor, J. 2014-2015 Spring
Monday Wednesday
2:15pm - 3:30pm
100-101K
Literature of Statistics
STATS 319 (section 1)

Literature study of topics in statistics and probability culminating in oral and written reports. May be repeated for credit.

Romano, J. 2014-2015 Spring
Wednesday
1:00pm - 1:50pm
250-108
Modern Spectral Analysis
STATS 333 (section 1)

Traditional spectral analysis encompassed Fourier methods and their elaborations, under the assumption of a simple superposition of sinusoids, independent of time. This...

Chui, C., Donoho, D. 2014-2015 Spring
Wednesday
2:15pm - 4:05pm
380-381T
Statistical and Machine Learning Methods for Genomics (BIO 268, BIOMEDIN 245, CS 373, GENE 245)
STATS 345 (section 1)

Introduction to statistical and computational methods for genomics. Sample topics include: expectation maximization, hidden Markov model, Markov chain Monte Carlo,...

Kundaje, A., Pritchard, J., Tang, H. 2014-2015 Spring
Monday Wednesday
10:30am - 11:50am
Topic: Monte Carlo
STATS 362 (section 1)

Random numbers and vectors: inversion, acceptance-rejection, copulas. Variance reduction: antithetics, stratification, control variates, importance sampling. MCMC: Markov...

Owen, A. 2014-2015 Spring
Monday Wednesday
2:15pm - 3:30pm
300-300
A Course in Bayesian Statistics (STATS 270)
STATS 370 (section 1)

Advanced-level Bayesian statistics. Topics: Discussion of the mathematical and theoretical foundation for Bayesian inferential procedures. Examination of the construction...

Wong, W. 2014-2015 Spring
Monday Wednesday
9:30am - 10:45am
200-107
Consulting Workshop
STATS 390 (section 1)

Skills required of practicing statistical consultants, including exposure to statistical applications. Students participate as consultants in the department's drop-in...

Fithian, W. 2014-2015 Spring
Friday
12:00pm - 12:50pm
Sequoia Hall 200
Consulting Workshop
STATS 390 (section 1)

Skills required of practicing statistical consultants, including exposure to statistical applications. Students participate as consultants in the department's drop-in...

Fithian, W. 2014-2015 Spring
Friday
12:00pm - 12:50pm
Sequoia Hall 200