Financial Planning & Analysis

• Department: Accounting (ACCT)
• Instructor: Igor Vaysman
  Info
• 1.5 points

Financial Planning & Analysis

Managerial Accounting (EMBA-Gl

• Department: Accounting (ACCT)
• 1.5 points

OPTIONS AND FUTURES

• Department: School of Professional Studies
• W 6:10 p.m.-8 p.m.
• Instructor: Christos I Giannikos
  Info
• 3 points

FINANCIAL ACCOUNTING

• Department: School of Professional Studies
• T 6:10 p.m.-8 p.m.
• T 8:10 p.m.-10 p.m.
• Instructor: Marc Blatter
  Info
• 3 points

FINANCIAL ACCOUNTING

FINANCIAL ACCTG (ONLINE)

• Department: School of Professional Studies
• W 8:30 p.m.-10 p.m.
• Instructor: Rebecca Shaffer
  Info
• 3 points

MANAGING HUMAN BEHAVIOR IN THE ORGANIZAT

• Department: School of Professional Studies
• R 6:10 p.m.-8 p.m.
• Instructor: Skye Cleary
  Info
• 3 points

MANAGING HUMAN BEHAVIOR IN THE ORGANIZAT

• Department: School of Professional Studies
• R 6:10 p.m.-8 p.m.
• R 8:10 p.m.-10 p.m.
• Instructor: Skye Cleary
  Info
• Instructor: Richard Kopelman
  CULPA: 8 Info
• Instructor: Skye Cleary
  Info
• Instructor: Erik Nelson
  Info
• 1.5 points

MANAGING HUMAN BEHAVIOR IN THE ORGANIZAT

MANAGING HUMAN BEHAVIOR ONLINE

• Department: School of Professional Studies
• W 8:30 p.m.-10 p.m.
• Instructor: Joshua L Burgher
  Info
• 3 points

INTRO TO THE MATH OF FINANCE

Prerequisites: MATH UN1202, MATH UN3027, STAT GR5203, or their equivalents.
The mathematics of finance, principally the problem of pricing of derivative securities, developed using only calculus and basic probability. Topics include mathematical models for financial instruments, Brownian motion, normal and lognormal distributions, the Black├╗Scholes formula, and binomial models.

• Department: Mathematics
• MW 7:40 p.m.-8:55 p.m.
• Instructor: Mikhail Smirnov
  CULPA: 16 Info
• 3 points

ADDICTION AND AGENCY

It is commonly believed that one cannot be blamed for actions that one is compelled to perform. Addiction is often taken to be an archetypical; case of a condition that can compel actions. But what is addiction? Is there a principled division between paradigmatic addictive behaviors such as heroin use and, e.g., excessive cell phone use? The answers to questions like these turn out to be highly controversial. Drawing on psychiatric as well as philosophical literatures, we will seek an analysis of the concept of addiction that can illuminate its moral significance. We will conclude by discussing arguments for skepticism about responsibility judgments more broadly. According to such arguments, none of us are responsible for anything, not because responsible action is incompatible with determinism, but because there is no principled explanation for why addicts would fail to be responsible which not overgeneralize to non-addicts.

• Department: Philosophy
• T 2:10 p.m.-4 p.m.
• 3 points

LEADING ALIGNMENT AND AGILITY

LDNG ALGMNT & AGILITY (ONLINE)

• Department: School of Professional Studies
• M 8:30 p.m.-10 p.m.
• Instructor: Ric Oslin
  Info
• 3 points

INTRODUCTION TO MARKETING

INTRO TO MARKETING

• Department: School of Professional Studies
• M 6:10 p.m.-8 p.m.
• Instructor: Asim Ansari
  CULPA: 3 h-index: 29 Info
• 3 points

INTRODUCTION TO MARKETING

INTRO TO MARKETING (ONLINE)

• Department: School of Professional Studies
• T 8:30 p.m.-10 p.m.
• Instructor: Lauri Harrison
  Info
• 3 points

GIVING & RECEIVING ACCTS OF SELF

GIVING & RECEIVING ACCOUNTS OF

• Department: School of Professional Studies
• R 3:10 p.m.-6 p.m.
• R 6:10 p.m.-9 p.m.
• M 12:10 p.m.-3 p.m.
• 4 points

MARKETING STRATEGY

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

MARKETING STRATEGY

MARKETING STRATEGY (ONLINE)

• Department: School of Professional Studies
• T 8:30 p.m.-10 p.m.
• Instructor: Andrew Blum
  Info
• 3 points

ILLNESS/DISABILITY NARRATIVES

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

DEVELOP/IMPLEMENTING NEW IDEAS

• Department: School of Professional Studies
• R 6:10 p.m.-8 p.m.
• Instructor: Grant J Son
  Info
• 3 points

DEVELOP/IMPLEMENTING NEW IDEAS

• Department: School of Professional Studies
• R 6:10 p.m.-8 p.m.
• Instructor: Grant J Son
  Info
• 3 points

SECURITY ANALYSIS

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

TOPICS IN AMERICAN CINEMA

TPCS AMER CINEMA: THE WESTERN

• Department: Film
• M 10 a.m.-1:50 p.m.
• Instructor: Cortland Rankin
  Info
• 3 points

THE LITERATURE OF ART

• Department: School of Professional Studies
• R 6:10 p.m.-8 p.m.
• Instructor: Rika Burnham
  Info
• 4 points

APPL ANALYTICS IN ORGANIZATIONAL CONTEXT

APP AN IN ORG CONTEXT

• Department: School of Professional Studies
• 3 points

APPL ANALYTICS IN ORGANIZATIONAL CONTEXT

APP AN IN ORG CONTEXT

• Department: School of Professional Studies
• 3 points

APPL ANALYTICS IN ORGANIZATIONAL CONTEXT

APP AN IN ORG CONTEXT

• Department: School of Professional Studies
• 3 points

GLOBAL EMERGING MARKETS

• Department: School of Professional Studies
• T 6:10 p.m.-8 p.m.
• Instructor: Panos Mourdoukoutas
  Wikipedia Info
• 3 points

VALUE-BASED ENTERPRISE RISK MGMT

VALUE-BASED ENTERPRSE RISK MGT

• Department: School of Professional Studies
• R 6:10 p.m.-8 p.m.
• T 8:10 p.m.-10 p.m.
• F 4:10 p.m.-6 p.m.
• Instructor: Sim Segal
  Info
• Instructor: Dave Raszeja
  Info
• Instructor: Rajeev Dave
  Info
• 3 points

SELF & OTHER IN CLIN ENCOUNTER

• Department: School of Professional Studies
• M 6:10 p.m.-8 p.m.
• Instructor: Michael E Davidovits
  Info
• 4 points

Decision Models

Decision Models (EMBA-Global C

• Department: Decision, Risk and Operations
• 1.5 points

STRATEGIC STORYTELLING

• Department: School of Professional Studies
• Instructor: Jane Praeger
  Info
• 1.5 points

APPLIED ANALYTICS FRAMEWORKS & METHODS

APP AN FRAMEWORKS & MTHDS

• Department: School of Professional Studies
• R 7 p.m.-8:30 p.m.
• M 7 p.m.-8:30 p.m.
• T 7 p.m.-8:30 p.m.
• 3 points

Managerial Economics

• Department: Business Economics
• Instructor: Matthew Backus
  h-index: 11 Info
• 1.5 points

Managerial Economics

• Department: Business Economics
• Instructor: Matthew Backus
  h-index: 11 Info
• Instructor: Andrea Prat
  Wikipedia h-index: 39 Info
• 1.5 points

Managerial Economics

• Department: Business Economics
• Instructor: Andrea Prat
  Wikipedia h-index: 39 Info
• 1.5 points

WORKSHOP ON POPULATION ORAL HEALTH

WRKSHP ON POPULATION ORAL HLTH

• Department: Dental and Oral Surgery
• Instructor: Burton L Edelstein
  Info
• 0 points

TRADITIONAL RISK & ERM PRACTICES

TRAD RISK & ERM PRAC

• Department: School of Professional Studies
• T 8:10 p.m.-10 p.m.
• M 8:10 p.m.-10 p.m.
• Instructor: James Bone
  Info
• Instructor: Chris Jackson
  Info
• 3 points

CERAMICS I

• Department: Visual Arts
• R 10 a.m.-4 p.m.
• Instructor: Leah R Wolff
  Info
• 3 points

WIRED NONPROFIT: SOC MEDIA STRAT & PRAC

• Department: School of Professional Studies
• S 9 a.m.-noon
• Instructor: Marcia Stepanek
  Info
• 3 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.
• Instructor: Irene Hueter
  CULPA: 9 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

DELIVERING THE STRAT MESSAGE

• Department: School of Professional Studies
• W 6:10 p.m.-8 p.m.
• F 9 a.m.-11 a.m.
• Instructor: Liza Gross
  Info
• Instructor: Jane Praeger
  Info
• 1.5 points

CO-CONSTRUCTING NARRATIVES

• Department: School of Professional Studies
• M 6:10 p.m.-9 p.m.
• 4 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
• R 8:10 p.m.-10 p.m.
• Instructor: Keith Lewis
  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
• F 11:40 a.m.-2:10 p.m.
• Instructor: Hammou Elbarmi
  CULPA: 1 Info
• 3 points

STAT INF/TIME-SERIES MODELLING

Prerequisites: STAT GR5205 or the equivalent.
Available to SSP, SMP  Modeling and inference for random processes, from natural sciences to finance and economics. ARMA, ARCH, GARCH and nonlinear models, parameter estimation, prediction and filtering.

• Department: Statistics
• TR 6:10 p.m.-7:25 p.m.
• S 10:10 a.m.-12:55 p.m.
• Instructor: Richard Davis
  CULPA: 3 h-index: 60 Info
• Instructor: Pawel Polak
  Info
• 3 points

STOCHASTC PROCSSES-APPLICTNS I

Prerequisites: STAT GR5203 or the equivalent.
Basics of continuous-time stochastic processes. Wiener processes. Stochastic integrals. Ito's formula, stochastic calculus. Stochastic exponentials and Girsanov's theorem. Gaussian processes. Stochastic differential equations. Additional topics as time permits.

• Department: Statistics
• MW 6:10 p.m.-7:25 p.m.
• TR 4:10 p.m.-5:25 p.m.
• Instructor: Ioannis Karatzas
  CULPA: 7 h-index: 58 Info
• Instructor: Lars T Nielsen
  h-index: 17 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 7:40 p.m.-8:55 p.m.
• Instructor: Yuchong Zhang
  Info
• 3 points

CAPITAL MARKETS & INVESTMENTS

Risk/return tradeoff, diversification and their role in the modern portfolio theory, their consequences for asset allocation, portfilio optimization.  Capitol Asset Pricing Model, Modern Portfolio Theory, Factor Models, Equities Valuation, definition and treatment of futures, options and fixed income securities will be covered.

• Department: Mathematics
• F 5 p.m.-7:20 p.m.
• Instructor: Alexei Chekhlov
  CULPA: 3 h-index: 10 Info
• 3 points

NARRATIVES-DEATH,LIVING,CARING

NARRATIVES, DEATH, LIVING & CA

• Department: School of Professional Studies
• T 6:10 p.m.-9 p.m.
• Instructor: Marsha Hurst
  Info
• 4 points

RESEARCH DESIGN

• Department: School of Professional Studies
• Instructor: Robert N Stinerock
  Info
• 3 points

RESEARCH DESIGN

• Department: School of Professional Studies
• 3 points

HEDGE FUNDS STRATEGIES & RISK

The hedge fund industry has continued to grow after the financial crisis, and hedge funds are increasingly important as an investable asset class for institutional investors as well as wealthy individuals. This course will cover hedge funds from the point of view of portfolio managers and investors. We will analyze a number of hedge fund trading strategies, including fixed income arbitrage, global macro, and various equities strategies, with a strong focus on quantitative strategies. We distinguish hedge fund managers from other asset managers, and discuss issues such as fees and incentives, liquidity, performance evaluation, and risk management. We also discuss career development in the hedge fund context.

• Department: Mathematics
• T 8:10 p.m.-10 p.m.
• Instructor: Eric Yeh
  Info
• 3 points

QUALITATIVE METHODS & RESEARCH

• Department: School of Professional Studies
• R 12:10 p.m.-3 p.m.
• Instructor: Edgar Rivera Colon
  Info
• 4 points

SCULPTURE I

• Department: Visual Arts
• F 10 a.m.-4 p.m.
• T 10 a.m.-4 p.m.
• Instructor: Jon Kessler
  CULPA: 5 Wikipedia Info
• Instructor: Stephen P Jackson
  Info
• 3 points

PRECLINICAL COMPREHENSIVE CARE I

PRECLINICAL DENTISTRY I

• Department: Dental and Oral Surgery
• Instructor: Roseanna Graham
  h-index: 4 Info
• 0 points

MANAGING INFORMATION & KNOWLEDGE

• Department: School of Professional Studies
• T 6:10 p.m.-8 p.m.
• Instructor: Guy St. Clair
  Wikipedia Info
• 3 points

MANAGING INFORMATION & KNOWLEDGE

• Department: School of Professional Studies
• T 6:10 p.m.-8 p.m.
• Instructor: Guy St. Clair
  Wikipedia Info
• 1.5 points

SCULPTURE III

• Department: Visual Arts
• M 10 a.m.-4 p.m.
• Instructor: Rirkrit Tiravanija
  Wikipedia Info
• 3 points

FINANCIAL RISK MGMT & REGULATION

FINANCIAL RISK MGMT & REGULATN

Prerequisites: Student expected to be mathematically mature and familiar with probability and statistics, arbitrage pricing theory, and stochastic processes.
  The course will introduce the notions of financial risk management, review the structure of the markets and the contracts traded, introduce risk measures such as VaR, PFE and EE, overview regulation of financial markets, and study a number of risk management failures. After successfully completing the course, the student will understand the basics of computing parametric VaR, historical VaR, Monte Carlo VaR, cedit exposures and CVA and the issues and computations associated with managing market risk and credit risk. The student will be familiar with the different categories of financial risk, current regulatory practices, and the events of financial crises, especially the most recent one.

• Department: Mathematics
• R 8:10 p.m.-10 p.m.
• Instructor: Harvey Stein
  h-index: None Info
• 3 points

OPERATIONAL RISK MANAGEMENT

OPERATIONAL RISK MGMT

• Department: School of Professional Studies
• T 6:10 p.m.-8 p.m.
• F 2:10 p.m.-4 p.m.
• R 8:10 p.m.-10 p.m.
• 3 points

FIXED INCOME PORTFOLIO MGMT

Prerequisites: Comfortable with algebra, calculus, probability, statistics, and stochastic calculus.
The course covers the fundamentals of fixed income portfolio management. Its goal is to help the students develop concepts and tools for valuation and hedging of fixed income securities within a fixed set of parameters. There will be an emphasis on understanding how an investment professional manages a portfolio given a budget and a set of limits.

• Department: Mathematics
• F 6:10 p.m.-8 p.m.
• Instructor: Rosanna Pezzo
  Info
• 3 points

GRANTS

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

PLANNED GIVING

• Department: School of Professional Studies
• M 6:10 p.m.-10 p.m.
• R 6:10 p.m.-8 p.m.
• Instructor: Neal Myerberg
  Info
• Instructor: Mohammad Zaidi
  Info
• 3 points

INSURANCE RISK MANAGEMENT

INSURANCE RISK MGMT

• Department: School of Professional Studies
• M 6:10 p.m.-8 p.m.
• W 8:10 p.m.-10 p.m.
• Instructor: Rich Lauria
  Info
• Instructor: Kevin Madigan
  Info
• 3 points

PRINTMAKING I: PHOTOGRAVURE

• Department: Visual Arts
• R 10 a.m.-4 p.m.
• Instructor: Craig Zammiello
  Info
• 3 points

BEGINNING VIDEO

• Department: Visual Arts
• T 1 p.m.-4 p.m.
• Instructor: Shelly Silver
  CULPA: 1 Info
• 3 points

MAFN FIELDWORK

This course provides an opportunity for MAFN students to engage in off-campus internships for academic credit that counts towards the degree. Graded by letter grade. Students need to secure an internship and get it approved by the instructor.

• Department: Mathematics
• Instructor: Lars T Nielsen
  h-index: 17 Info
• 1-3 points

UNPAID INTERNSHIP

This course provides an opportunity for MAFN students to engage in unpaid internships for academic credit on a pass / fail basis. Students need to secure an internship and get it approved by the instructor. For unpaid internships only.

• Department: Mathematics
• Instructor: Lars T Nielsen
  h-index: 17 Info
• 1 points

SYSTEMIC RISK

• Department: School of Professional Studies
• F 10:10 a.m.-noon
• W 8:10 p.m.-10 p.m.
• Instructor: Venkat Venkatasubramanian
  h-index: 58 Info
• Instructor: H.S. Bob Kostakopoulos
  Info
• 3 points

ANALYTICS & LEADING CHANGE

ANALYTICS & LEADING CHNG

• Department: School of Professional Studies
• 3 points

ANALYTICS & LEADING CHANGE

ANALYTICS & LEADING CHNG

• Department: School of Professional Studies
• 3 points

PHOTOGRAPHY: PHOTO I

• Department: Visual Arts
• MW 10:30 a.m.-1 p.m.
• MW 2:30 p.m.-5 p.m.
• Instructor: Thomas Roma
  CULPA: 18 Wikipedia Info
• 3 points

PROBABILITY & STAT FOR DATA SC

Prerequisites: Calculus
This course covers the following topics: Fundamentals of probability theory and statistical inference used in data science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference; point and confidence interval estimation, hypothesis tests, linear regression.

• Department: Statistics
• TR 6:10 p.m.-7:25 p.m.
• Instructor: Sheung Chi Yam
  Info
• 3 points

STAT INFERENCE & MODELING

Prerequisites: working knowledge of calculus and linear algebra (vectors and matrices), and STAT GR5203 or the equivalent.
In this course, we will systematically cover fundamentals of statistical inference and testing, and give an introduction to statistical modeling. The first half of the course will be focused on inference and teesting, covering topics such as maximum likelihood estimates, hypothesis testing, likelihood ratio test, Bayesian inference, etc. The second half of the course will provide introduction to statistical modeling via introductory lectures on linear regression models, generalized linear regression models, nonparametric regression. and statistical computing. Throughpout the course, real-data examples will be used in lecture discussion and homework problems. This course lays the foundation, preparing the MA in Data Science studnets, for other courses in machine learning, data mining and visualization.

• Department: Statistics
• MW 6:10 p.m.-8:55 p.m.
• Instructor: Banu Baydil
  CULPA: 10 Info
• 3 points

INTRODUCTION TO DATA SCIENCE

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

International Assignment: Arge

• Department: Business
• 3 points

INT'L ASSIGNMENT: DUBAI

International Assignment: Duba

• Department: Business
• 3 points