NONFICTION WORKSHOP

• Department: Writing
• T 1:05 p.m.-4:05 p.m.
• T 10 a.m.-1 p.m.
• T 2:20 p.m.-5:20 p.m.
• M 1:05 p.m.-4:05 p.m.
• M 3:10 p.m.-6:10 p.m.
• M 1:05 p.m.-4:05 p.m.
• Instructor: Lis Harris
  Wikipedia Info
• Instructor: Leslie S Jamison
  Wikipedia Info
• Instructor: Richard Locke
  CULPA: 6 Wikipedia Info
• Instructor: Phillip Lopate
  CULPA: 1 Wikipedia Info
• Instructor: Benjamin Taylor
  Wikipedia Info
• Instructor: Brenda Wineapple
  Wikipedia Info
• 6 points

Global Economic Environment

• Department: Business Economics
• 2.2 points

Global Economic Environment

• Department: Business Economics
• 2.2 points

Global Economic Environment

• Department: Business Economics
• 2.2 points

Global Economic Environment

• Department: Business Economics
• 2.2 points

Global Economic Environment

• Department: Business Economics
• 3 points

Global Economic Environment

• Department: Business Economics
• 3 points

ADVANCED CONFLICT RESOLUTION

• Department: School of Professional Studies
• W 6:10 p.m.-9 p.m.
• Instructor: Jose Pascal da Rocha
  h-index: 6 Info
• 3 points

PROBABILITY

Prerequisites: At least one semester of calculus.
A calculus-based introduction to probability theory. Topics covered include random variables, conditional probability, expectation, independence, Bayes' rule, important distributions, joint distributions, moment generating functions, central limit theorem, laws of large numbers and Markov's inequality.

• Department: Statistics
• MW 6:10 p.m.-7:25 p.m.
• Instructor: Dan Wang
  CULPA: 3 h-index: 13 Info
• 3 points

STATISTICAL INFERENCE

Prerequisites: STAT GR5203 or the equivalent, and two semesters of calculus.
Calculus-based introduction to the theory of statistics. Useful distributions, law of large numbers and central limit theorem, point estimation, hypothesis testing, confidence intervals, maximum likelihood, likelihood ratio tests, nonparametric procedures, theory of least squares and analysis of variance.

• Department: Statistics
• MW 6:10 p.m.-7:25 p.m.
• Instructor: Abolfazal Safikhani
  Info
• 3 points

ADVANCED NEGOTIATION

• Department: School of Professional Studies
• S 9 a.m.-5 p.m.
• Instructor: Sequoia Stalder
  Info
• 3 points

NARRATIVE ETHICS

• Department: School of Professional Studies
• T 4:10 p.m.-6 p.m.
• Instructor: Danielle R Spencer
  h-index: 5 Info
• 4 points

LINEAR REGRESSION MODELS

Prerequisites: STAT GR5203 and GR5204 or the equivalent.
Theory and practice of regression analysis, Simple and multiple regression, including testing, estimation, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. Extensive use of the computer to analyse data.

• Department: Statistics
• TR 7:40 p.m.-8:55 p.m.
• Instructor: Ronald Neath
  CULPA: 13 Info
• 3 points

FNDATION CLINICAL MED: TUTORIALS

FNDATNS CLIN MED: TUTORIALS

• Department: Medicine
• 0 points

ADVANCED MEDIATION

• Department: School of Professional Studies
• S 9 a.m.-5 p.m.
• Instructor: Jenny Besch
  Info
• 3 points

ELEMENTARY STOCHASTIC PROCESSE

ELEMENTRY STOCHASTIC PROCESSES

Corequisites: GR5203 or the equivalent.
Review of elements of probability theory. Poisson processes. Renewal theory. Wald's equation. Introduction to discrete and continuous time Markov chains. Applications to queueing theory, inventory models, branching processes.

• Department: Statistics
• MW 11:40 a.m.-12:55 p.m.
• Instructor: Mark Brown
  CULPA: 18 Wikipedia Info
• 3 points

CONF RESOLUTION/DYNAMIC SYSTEM

• Department: School of Professional Studies
• SUUS 9 a.m.-5 p.m.
• Instructor: Naira Musallam
  Info
• 3 points

MICROECONOMIC ANALYSIS II

• Department: Economics
• MW 4:10 p.m.-5:25 p.m.
• Instructor: Mark Dean
  CULPA: 2 h-index: 26 Info
• 3 points

MACROECONOMICS ANALYSIS II

• Department: Economics
• MW 1:10 p.m.-2:25 p.m.
• Instructor: Irasema Alonso
  CULPA: 12 Info
• 3 points

ENVIRNMNT CONFL/RES STRATEGIES

• Department: School of Professional Studies
• M 6:10 p.m.-9 p.m.
• Instructor: Joshua Fisher
  h-index: 8 Info
• 3 points

DIRECTING II

• Department: Film
• W 2 p.m.-5 p.m.
• R 2 p.m.-5 p.m.
• T 10 a.m.-1 p.m.
• W 10 a.m.-1 p.m.
• R 6 p.m.-9 p.m.
• M 10 a.m.-1 p.m.
• Instructor: Bette Gordon
  Wikipedia Info
• 3 points

NARRATIVE,HEALTH & SOC JUSTICE

• Department: School of Professional Studies
• W 12:10 p.m.-2 p.m.
• Instructor: Sayantani T Dasgupta
  CULPA: 2 Info
• 4 points

TIME SERIES ANALYSIS

Prerequisites: STAT GR5205
Least squares smoothing and prediction, linear systems, Fourier analysis, and spectral estimation. Impulse response and transfer function. Fourier series, the fast Fourier transform, autocorrelation function, and spectral density. Univariate Box-Jenkins modeling and forecasting. Emphasis on applications. Examples from the physical sciences, social sciences, and business. Computing is an integral part of the course.

• Department: Statistics
• MW 7:40 p.m.-8:55 p.m.
• S 2:40 p.m.-5:25 p.m.
• Instructor: Abolfazal Safikhani
  Info
• Instructor: Rongning Wu
  CULPA: 1 Info
• 3 points

NONPARAMETRIC STATISTICS

Prerequisites: STAT GR5205
Statistical inference without parametric model assumption. Hypothesis testing using ranks, permutations, and order statistics. Nonparametric analogs of analysis of variance. Non-parametric regression, smoothing and model selection.

• Department: Statistics
• MW 1:10 p.m.-2:25 p.m.
• Instructor: Pawel Polak
  Info
• 3 points

MULTIVARIATE STAT INFERENCE

Prerequisites: STAT GR5205.
Multivariate normal distribution, multivariate regression and classification; canonical correlation; graphical models and Bayesian networks; principal components and other models for factor analysis; SVD; discriminant analysis; cluster analysis.

• Department: Statistics
• TR 1:10 p.m.-2:25 p.m.
• Instructor: Pawel Polak
  Info
• 3 points

BAYESIAN STATISTICS

Prerequisites: STAT GR5205
Bayesian vs frequentist, prior and posterior distributions, conjugate priors, informative and non-informative prior subjective and objective bayes, oneand two sample problems, models for normal data, models for binary data, multivariate normal shrinkage, bayesian linear models, bayesian computation (start early), MCMC algorithms, the Gibbs sampler, hierarchical models, empirical bayes, hypothesis testing, bayes factors, model selection, software: R and WinBUGS

• Department: Statistics
• TR 6:10 p.m.-7:25 p.m.
• Instructor: Ronald Neath
  CULPA: 13 Info
• 3 points

DELIVERING THE STRAT MESSAGE

• Department: School of Professional Studies
• W 6:10 p.m.-8 p.m.
• Instructor: Liza Gross
  Info
• 3 points

THE EARTH'S CLIMATE SYSTEM

• Department: School of Professional Studies
• T 4:10 p.m.-6 p.m.
• Instructor: Benjamin I Cook
  CULPA: 3 h-index: 56 Info
• 3 points

SURVIVAL ANALYSIS

Prerequisites: STAT GR5205
Survival distributions, types of censored data, estimation for various survival models, nonparametric estimation of survival distributions, the proportional hazard and accelerated lifetime models for regression analysis with failure-time data. Extensive use of the computer.

• Department: Statistics
• MW 6:10 p.m.-7:25 p.m.
• Instructor: Michael Shnaidman
  CULPA: 7 Info
• 3 points

GENERALIZED LINEAR MODELS

Prerequisites: STAT GR5205
Statistical methods for rates and proportions, ordered and nominal categorical responses, contingency tables, odds-ratios, exact inference, logistic regression, Poisson regression, generalized linear models.

• Department: Statistics
• TR 4:10 p.m.-5:25 p.m.
• Instructor: Michael Sobel
  CULPA: 5 Wikipedia h-index: 35 Info
• 3 points

DIRECTING ACTORS II

• Department: Film
• W 10 a.m.-1 p.m.
• R 10 a.m.-1 p.m.
• M 6 p.m.-9 p.m.
• T 10 a.m.-1 p.m.
• R 10 a.m.-1 p.m.
• M 2 p.m.-5 p.m.
• Instructor: Jon Shear
  Info
• Instructor: John Rubin
  Info
• Instructor: Adrienne Weiss
  Info
• Instructor: Jodie Markell
  Wikipedia Info
• Instructor: Jose Santana
  Wikipedia Info
• Instructor: Erica Fae
  Info
• 3 points

MAJOR & LEADERSHIP GIVING

ANNUAL,CPTL CMPGNS & MAJ GIFTS

• Department: School of Professional Studies
• W 4:10 p.m.-6 p.m.
• Instructor: Robin H Rosenbluth
  Info
• 3 points

STATISTICAL MACHINE LEARNING

Prerequisites: STAT GR5206 or the equivalent.
The course will provide an introduction to Machine Learning and its core models and algorithms. The aim of the course is to provide students of statistics with detailed knowledge of how Machine Learning methods work and how statistical models can be brought to bear in computer systems - not only to analyze large data sets, but to let computers perform tasks that traditional methods of computer science are unable to address. Examples range from speech recognition and text analysis through bioinformatics and medical diagnosis. This course provides a first introduction to the statistical methods and mathematical concepts which make such technologies possible.

• Department: Statistics
• TR 6:10 p.m.-7:25 p.m.
• TR 7:40 p.m.-8:55 p.m.
• Instructor: Linxi Liu
  Info
• Instructor: Cynthia Rush
  CULPA: 1 h-index: 12 Info
• 3 points

APPLIED DATA SCIENCE

Prerequisites: STAT GR5206 or the equivalent.
This course will incorporate knowledge and skills covered in a statistical curriculum with topics and projects in data science. Programming will covered using existing tools in R. Computing best practices will be taught using test-driven development, version control, and collaboration. Students finish the class with a portfolio on GitHub, and deeper understanding of several core statistical/machine-learning algorithms. Bi-weekly project cycles throughout the semester provide students extensive hands-on experience with various data-driven applications.

• Department: Statistics
• F 2:40 p.m.-5:25 p.m.
• Instructor: Tian Zheng
  CULPA: 13 Wikipedia h-index: 25 Info
• 3 points

BUSINESS OF ISSUES

• Department: School of Professional Studies
• T 4:10 p.m.-6 p.m.
• Instructor: Hayley Berlent
  Info
• 3 points

EXTERNAL STAKEHOLDER REQUIREMENTS

• Department: School of Professional Studies
• T 6:10 p.m.-8 p.m.
• W 8:10 p.m.-10 p.m.
• T 8:10 p.m.-10 p.m.
• R 8:10 p.m.-10 p.m.
• Instructor: Rich Lauria
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• Instructor: Isabel Rohrbeck
  Info
• Instructor: Neil Strauss
  Wikipedia Info
• 3 points

PROG FOR QUANT & COMP FINANCE

This course covers features of the C++ programming language which are essential in quantitative/computational finance and its applications. We start by covering basic C++ programming features and then move to some more advance features. We utilize these features for financial engineering and quantitative finance applications primarily for pricing of financial derivatives and computational finance. Those applications include transform techniques, Monte Carlo simulation, calibration and parameter estimation techniques.

• Department: Mathematics
• W 1:10 p.m.-3:40 p.m.
• Instructor: Ali Hirsa
  h-index: 8 Info
• 3 points

STATISTICAL METHODS IN FINANCE

Prerequisites: STAT GR5204 or the equivalent. STAT GR5205 is recommended.
A fast-paced introduction to statistical methods used in quantitative finance. Financial applications and statistical methodologies are intertwined in all lectures. Topics include regression analysis and applications to the Capital Asset Pricing Model and multifactor pricing models, principal components and multivariate analysis, smoothing techniques and estimation of yield curves statistical methods for financial time series, value at risk, term structure models and fixed income research, and estimation and modeling of volatilities. Hands-on experience with financial data.

• Department: Statistics
• S 8:40 a.m.-11:40 a.m.
• Instructor: Zhiliang Ying
  CULPA: 1 Wikipedia h-index: 62 Info
• 3 points

STOCHASTIC PROCESSES FOR FIN

Prerequisites: STAT GR5203 or the equivalent.
This course covers theory of stochastic processes applied to finance. It covers concepts of Martingales, Markov chain models, Brownian motion. Stochastic Integration, Ito's formula as a theoretical foundation of processes used in financial modeling. It also introduces basic discrete and continuous time models of asset price evolutions in the context of the following problems in finance: portfolio optimization, option pricing, spot rate interest modeling.

• Department: Statistics
• TR 2:40 p.m.-3:55 p.m.
• Instructor: David A Rios
  CULPA: 16 Info
• 3 points

STOCHASTIC METHODS IN FINANCE

Prerequisites: STAT GR5264
Available to SSP, SMP. Mathematical theory and probabilistic tools for modeling and analyzing security markets are developed. Pricing options in complete and incomplete markets, equivalent martingale measures, utility maximization, term structure of interest rates.

• Department: Statistics
• MW 4:10 p.m.-5:25 p.m.
• TR 6:10 p.m.-7:25 p.m.
• Instructor: Yuchong Zhang
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• Instructor: Irene Hueter
  CULPA: 9 Info
• 3 points

ADVANCED DATA ANALYSIS

Prerequisites: W4315 and either another statistics course numbered above the 4200 or permission of instructor.
Required for the major in statistics. Data analysis using a computer statistical package and selected exploratory data analysis subroutines. Topics include editing of data for errors, exploratory and standard techniques for one-way analysis of variance, linear regression, and two-way analysis of variance. Material is presented in case-study format.

• Department: Statistics
• F 10:10 a.m.-12:40 p.m.
• Instructor: Hammou Elbarmi
  CULPA: 1 Info
• 3 points

HISTORY OF BIOETHICS

HISTORY OF BIOETHICS ONLINE

• Department: School of Professional Studies
• T 5:30 p.m.-7 p.m.
• Instructor: James K Colgrove
  CULPA: 9 Info
• 3 points

HISTORY OF BIOETHICS

HISTORY OF BIOETHICS ONLINE

• Department: School of Professional Studies
• T 5:30 p.m.-7 p.m.
• Instructor: James K Colgrove
  CULPA: 9 Info
• 0 points

STRATEGIC COMM FOR RISK PROFESSIONALS

STRAT COMM FOR RISK PROF

• Department: School of Professional Studies
• M 8:10 p.m.-10 p.m.
• T 8:10 p.m.-10 p.m.
• W 8:10 p.m.-10 p.m.
• R 8:10 p.m.-10 p.m.
• F 2:10 p.m.-4 p.m.
• Instructor: Don Waisanen
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• Instructor: Gino Perrotte
  Info
• Instructor: Kate Sweeney
  Info
• Instructor: Joseph D Ross
  Info
• Instructor: Rod Carveth
  Info
• 3 points

Corporate Finance

• Department: Finance
• 3 points

Corporate Finance

• Department: Finance
• 3 points

QUALITATIVE METHODS & RESEARCH

• Department: School of Professional Studies
• M 6:10 p.m.-9 p.m.
• Instructor: Edgar Rivera Colon
  Info
• 4 points

SCULPTURE I

• Department: Visual Arts
• F 10 a.m.-4 p.m.
• M 10 a.m.-4 p.m.
• Instructor: Jon Kessler
  CULPA: 5 Wikipedia Info
• Instructor: Pooneh Maghazehe
  Info
• 3 points

POETRY WORKSHOP

• Department: Writing
• R 1:35 p.m.-4:35 p.m.
• W 3:10 p.m.-6:10 p.m.
• T 4 p.m.-7 p.m.
• Instructor: Mark Bibbins
  Wikipedia Info
• Instructor: Lucie Brock-Broido
  Wikipedia Info
• Instructor: Richard Howard
  Wikipedia Info
• 6 points

CREATIVE EXP & CHAN ARCH CAPSTONE

CREATIVE EXPRESSION & CHANNEL

• Department: School of Professional Studies
• 3 points

SCULPTURE II

• Department: Visual Arts
• T 10 a.m.-4 p.m.
• Instructor: Marc Robinson
  Info
• 3 points

BUSINESS ANALYTICS MANAGEMENT

BUSINESS ANALYTICS MGMT

• Department: School of Professional Studies
• W 8 p.m.-9:30 p.m.
• Instructor: Jeanne G Harris
  Wikipedia h-index: 27 Info
• 3 points

ROLE OF THE PRODUCER

• Department: Film
• W 6 p.m.-9 p.m.
• Instructor: Jack Lechner
  Info
• 1.5 points

NETWORKS & COLLABORATION

• Department: School of Professional Studies
• R 8 p.m.-10 p.m.
• Instructor: Katrina Pugh
  h-index: 4 Info
• 3 points

LAW FOR KNOWLDGE INNOVATIONS & PRACTICES

LAW FOR KNWL INNOV & PRAC

• Department: School of Professional Studies
• R 7 p.m.-8:30 p.m.
• Instructor: Anne Kershaw
  Info
• 3 points

EMPIRICAL METHODS IN BIOETHICS

• Department: School of Professional Studies
• T 4 p.m.-5:30 p.m.
• Instructor: Nora Jacobson
  Info
• 3 points

ECONOMICS POLICY ANALYSIS

• Department: Economics
• R 12:10 p.m.-2 p.m.
• 3 points

PHILOSOPHY OF BIOETHICS

PHIL OF BIOETHICS (ONLINE)

• Department: School of Professional Studies
• M 6:30 p.m.-8:30 p.m.
• Instructor: Arthur Kuflik
  Info
• 3 points

PHILOSOPHY OF BIOETHICS

• Department: School of Professional Studies
• M 6:30 p.m.-8:30 p.m.
• Instructor: Arthur Kuflik
  Info
• 0 points

STRATEGIC RISK MANAGEMENT

STRATEGIC RISK MGMT

• Department: School of Professional Studies
• W 6:10 p.m.-8 p.m.
• R 8:10 p.m.-10 p.m.
• F 2:10 p.m.-4 p.m.
• F 4:10 p.m.-6 p.m.
• Instructor: Mike Cohen
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• Instructor: Conrad Fernandes
  Info
• Instructor: H.S. Bob Kostakopoulos
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• Instructor: Cecil R House
  Info
• 3 points

NONPROFIT FINANCIAL MGMT

• Department: School of Professional Studies
• M 6:10 p.m.-8 p.m.
• Instructor: Odell Mays
  Info
• 3 points

ANALYTICS OF GLOBAL SPORTS

• Department: School of Professional Studies
• T 4:10 p.m.-6 p.m.
• Instructor: Bret Myers
  Wikipedia Info
• 3 points

SUSTAINABLE INVESTING & ECONOMIC GROWTH

• Department: School of Professional Studies
• T 6:10 p.m.-8 p.m.
• Instructor: Satyajit Bose
  CULPA: 4 Info
• 3 points

LAW & BIOETHICS

• Department: School of Professional Studies
• T 6:10 p.m.-8 p.m.
• W 7:30 p.m.-9 p.m.
• Instructor: David N Hoffman
  h-index: 2 Info
• 3 points

LAW & BIOETHICS

• Department: School of Professional Studies
• W 7:30 p.m.-9 p.m.
• Instructor: David N Hoffman
  h-index: 2 Info
• 0 points

REPRODUCTIVE ETHICS

• Department: School of Professional Studies
• W 12:10 p.m.-2 p.m.
• Instructor: Arthur Kuflik
  Info
• 3 points

DIGITAL STRATEGIES

FC BAYERN MUNICH

• Department: School of Professional Studies
• W 6:10 p.m.-8 p.m.
• Instructor: Coral Marshall
  Info
• 3 points

NEUROSCIENCE & ETHICS

• Department: School of Professional Studies
• W 4:10 p.m.-6 p.m.
• Instructor: Carl Erik Fisher
  h-index: 14 Info
• 3 points