2014-2015 2015-2016 2016-2017 2017-2018 2018-2019
 Browseby subject... Scheduleview...

# 1 - 10 of 42 results for: STATS

## STATS 60:Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum | Units: 5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR | Grading: Letter or Credit/No Credit

## STATS 160:Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 60)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum | Units: 5 | Grading: Letter or Credit/No Credit

## STATS 191:Introduction to Applied Statistics

Statistical tools for modern data analysis. Topics include regression and prediction, elements of the analysis of variance, bootstrap, and cross-validation. Emphasis is on conceptual rather than theoretical understanding. Applications to social/biological sciences. Student assignments/projects require use of the software package R. Recommended: 60, 110, or 141.
Terms: Win | Units: 3-4 | UG Reqs: GER:DB-Math, WAY-AQR | Grading: Letter or Credit/No Credit
Instructors: Taylor, J. (PI)

## 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; Neyman-Pearson theory. Bayesian analysis; maximum likelihood, large sample theory. Prerequisite: 116.
Terms: Win, Sum | Units: 3 | Grading: Letter or Credit/No Credit

## STATS 211:Meta-research: Appraising Research Findings, Bias, and Meta-analysis (HRP 206, MED 206)

Open to graduate, medical, and undergraduate students. Appraisal of the quality and credibility of research findings; evaluation of sources of bias. Meta-analysis as a quantitative (statistical) method for combining results of independent studies. Examples from medicine, epidemiology, genomics, ecology, social/behavioral sciences, education. Collaborative analyses. Project involving generation of a meta-research project or reworking and evaluation of an existing published meta-analysis. Prerequisite: knowledge of basic statistics.
Terms: Win | Units: 3 | Grading: Medical Satisfactory/No Credit

## STATS 213:Introduction to Graphical Models (STATS 313)

Multivariate Normal Distribution and Inference, Wishart distributions, graph theory, probabilistic Markov models, pairwise and global Markov property, decomposable graph, Markov equivalence, MLE for DAG models and undirected graphical models, Bayesian inference for DAG models and undirected graphical models. Prerequisites: STATS 217, STATS 200 (preferably STATS 300A), MATH 104 or equivalent class in linear algebra.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit

## STATS 216:Introduction to Statistical Learning

Overview of supervised learning, with a focus on regression and classification methods. Syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis;cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; Some unsupervised learning: principal components and clustering (k-means and hierarchical). Computing is done in R, through tutorial sessions and homework assignments. This math-light course is offered via video segments (MOOC style), and in-class problem solving sessions. Prerequisites: first courses in statistics, linear algebra, and computing.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit

## STATS 217:Introduction to Stochastic Processes

Discrete and continuous time Markov chains, poisson processes, random walks, branching processes, first passage times, recurrence and transience, stationary distributions. Non-Statistics masters students may want to consider taking STATS 215 instead. Prerequisite: STATS 116 or consent of instructor.
Terms: Win, Sum | Units: 2-3 | Grading: Letter or Credit/No Credit

## STATS 231:Statistical Learning Theory (CS 229T)

(Same as STATS 231) How do we formalize what it means for an algorithm to learn from data? This course focuses on developing mathematical tools for answering this question. We will present various common learning algorithms and prove theoretical guarantees about them. Topics include online learning, kernel methods, generalization bounds (uniform convergence), and spectral methods. Prerequisites: A solid background in linear algebra and probability theory, statistics and machine learning ( STATS 315A or CS 229). Convex optimization ( EE 364a) is helpful but not required.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Liang, P. (PI)

## STATS 243:Financial Models and Statistical Methods in Active Risk Management (CME 243)

(SCPD students register for 243P.) Market risk and credit risk, credit markets. Back testing, stress testing and Monte Carlo methods. Logistic regression, generalized linear models and generalized mixed models. Loan prepayment and default as competing risks. Survival and hazard functions, correlated default intensities, frailty and contagion. Risk surveillance, early warning and adaptive control methodologies. Banking and bank regulation, asset and liability management. Prerequisite: STATS 240 or equivalent.
Terms: Win | Units: 2-4 | Grading: Letter or Credit/No Credit
Instructors: Lai, T. (PI)
Filter Results:
term offered
 Autumn Winter Spring Summer
updating results...
number of units
 1 unit 2 units 3 units 4 units 5 units >5 units
updating results...
time offered
 early morning (before 10am) morning (10am-12pm) lunchtime (12pm-2pm) afternoon (2pm-5pm) evening (after 5pm)
updating results...
days
 Monday Tuesday Wednesday Thursday Friday Saturday Sunday
updating results...
UG Requirements (GERs)
 WAY-A-II WAY-AQR WAY-CE WAY-ED WAY-ER WAY-FR WAY-SI WAY-SMA Language Writing 1 Writing 2 Writing SLE DB:Hum DB:Math DB:SocSci DB:EngrAppSci DB:NatSci EC:EthicReas EC:GlobalCom EC:AmerCul EC:Gender IHUM1 IHUM2 IHUM3
updating results...
component
 Lecture (LEC) Seminar (SEM) Discussion Section (DIS) Laboratory (LAB) Lab Section (LBS) Activity (ACT) Case Study (CAS) Colloquium (COL) Workshop (WKS) Independent Study (INS) Intro Dial, Sophomore (IDS) Intro Sem, Freshman (ISF) Intro Sem, Sophomore (ISS) Internship (ITR) Arts Intensive Program (API) Language (LNG) Practicum (PRA) Practicum (PRC) Research (RES) Sophomore College (SCS) Thesis/Dissertation (T/D)
updating results...
career