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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) |
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 |