Industrial Engineering and Operations Research

IEME E4200 HUMAN-CENTERED DESIGN AND INNOVATION. 3.00 points.

Prerequisites: Instructor's permission.
Open to SEAS graduate and advanced undergraduate students, Business School, and GSAPP. Students from other schools may apply. Fast-paced introduction to human-centered design. Students learn the vocabulary of design methods, understanding of design process. Small group projects to create prototypes. Design of simple product, more complex systems of products and services, and design of business

IEME E4201 Human-Centered Design and Innovation for MBAxMS Program. 1.50 point.

Introduces human-centered design process. Includes learning from and evaluating with customers, creating new ideas collaboratively as part of a team, simple ways to prototype both physical and digital experience, and presenting ideas with impact. Develop skills and experience by practicing process as they engage in various design challenges

IEME E4310 MANUFACTURING ENTERPRISE. 3.00 points.

Lect: 3.

Prerequisites: Refer to course syllabus.
The strategies and technologies of global manufacturing and service enterprises. Connections between the needs of a global enterprise, the technology and methodology needed for manufacturing and product development, and strategic planning as currently practiced in industry

IEME E4505 Frontiers of Tough Tech. 3.00 points.

Survey of frontier research in technological areas to address world's most important challenges. Areas include advanced materials, biotech, and sustainability and climate. Convergence of breakthrough research with entrepreneurship, industry, and policy

IEME E4810 INTRO-HUMANS IN SPACE FLIGHT. 3.00 points.

Prerequisites: Department permission and knowledge of MATLAB or equivalent.
Introduction to human spaceflight from a systems engineering perspective. Historical and current space programs and spacecraft. Motivation, cost, and rationale for human space exploration. Overview of space environment needed to sustain human life and health, including physiological and psychological concerns in space habitat. Astronaut selection and training processes, spacewalking, robotics, mission operations, and future program directions. Systems integration for successful operation of a spacecraft. Highlights from current events and space research, Space Shuttle, Hubble Space Telescope, and International Space Station (ISS). Includes a design project to assist International Space Station astronauts

IEOR E Fieldwork. 0 points.

IEOR E1000 Frontiers in Operations Research and Data Analytics. 1.00 point.

Introductory course for overview of modern approaches and ideas of operations research and data analytics. Through a series of interactive sessions, students engage in activities exploring OR topics with various faculty members from the IEOR department

IEOR E2000 Data Engineering with Python. 3.00 points.

Prerequisites: ENGI E1006; Or competency in Python.
Introduction to essential data engineering methods. Potential topics include Arrays, Linked Lists, Stacks and Queues, Trees and Graphs, Hash Tables, Search Algorithms and Efficiency, Relational databases, SQL, NoSQL, and Data Wrangling. Practice both theory and applications using Python programming

IEOR E2261 ACCOUNTING AND FINANCE. 3.00 points.

Lect: 3.

Prerequisites: (ECON UN1105) ECON W1105
For undergraduates only. Examines the fundamental concepts of financial accounting and finance, from the perspective of both managers and investors. Key topics covered include principles of accrual accounting; recognizing and recording accounting transactions; preparation and analysis of financial statements; ratio analysis; pro-forma projections; time value of money (present values, future values and interest/discount rates); inflation; discounted-cash-flow (DCF) project evaluation methods; deterministic and probabilistic measures of risk; capital budgeting

IEOR E3106 STOCHASTIC SYSTEMS AND APPLICATIONS. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E3658) and IEOR E3658; For undergraduates only. Required for all undergraduate students majoring in IE, OR:EMS, OR:FE, and OR. Must be taken during or before the fifth semester.
Some of the main stochastic models used in engineering and operations research applications: discrete-time Markov chains, Poisson processes, birth and death processes and other continuous Markov chains, renewal reward processes. Applications: queueing, reliability, inventory, and finance. IEOR E3106 must be completed by the fifth term. Only students with special academic circumstances may be allowed to take these courses in alternative semesters with the consultation of CSA and Departmental advisers

IEOR E3402 PRODUCTN-INVENTORY PLAN-CONTRL. 4.00 points.

Lect: 3. Recit: 1.

Prerequisites: (IEOR E3608) and (IEOR E3658) and IEOR E3608 AND IEOR E3658
For undergraduates only. Required for all undergraduate students majoring in IE, OR:EMS, OR:FE, and OR. Must be taken during (or before) the sixth semester. Inventory management and production planning. Continuous and periodic review models: optimal policies and heuristic solutions, deterministic and probabilistic demands. Material requirements planning. Aggregate planning of production, inventory, and work force. Multi-echelon integrated production-inventory systems. Production scheduling. Term project. Recitation section required

IEOR E3404 SIMULATION MODELING AND ANALYSIS. 4.00 points.

Prerequisites: (IEOR E3658) and (IEOR E4307) and Knowledge of a programming language such as Python, C, C++ or Matlab
Corequisites: IEOR E3106
It is strongly advised that Stochastic modeling (IEOR E3106 or IEOR E4106) be taken before this course. This is an introductory course to simulation, a statistical sampling technique that uses the power of computers to study complex stochastic systems when analytical or numerical techniques do not suffice. The course focuses on discrete-event simulation, a general technique used to analyze a model over time and determine the relevant quantities of interest. Topics covered in the course include the generation of random numbers, sampling from given distributions, simulation of discrete-event systems, output analysis, variance reduction techniques, goodness of fit tests, and the selection of input distributions. The first half of the course is oriented toward the design and implementation of algorithms, while the second half is more theoretical in nature and relies heavily on material covered in prior probability courses. The teaching methodology consists of lectures, recitations, weekly homework, and both in-class and take-home exams. Homework almost always includes a programming component for which students are encouraged to work in teams

IEOR E3608 FOUNDATIONS OF OPTIMIZATION. 3.00 points.

Lect: 3.

Prerequisites: (MATH UN2010) MATH V2010; Corequisite: Data structures.
Corequisites: COMS W3134,COMS W3137
This first course in optimization focuses on theory and applications of linear optimization, network optimization, and dynamic programming

IEOR E3609 ADVANCED OPTIMIZATION. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E3608) IEOR E3608
For undergraduates only. Required for all undergraduate students majoring in IE, OR:EMS, OR:FE, and OR. This is a follow-up to IEOR E3608 and will cover advanced topics in optimization, including integer optimization, convex optimization, and optimization under uncertainty, with a strong focus on modeling, formulations, and applications

IEOR E3658 PROBABILITY FOR ENGINEERS. 3.00 points.

Lect: 3.

Prerequisites: Calculus. For undergraduates only. Required for OR:FE concentration. Must be taken during or before third semester. Students who take IEOR E3658 may not take W4150 due to significant overlap. Recommended: strong mathematical skills.
Introductory course to probability theory and does not assume any prior knowledge of subject. Teaches foundations required to use probability in applications, but course itself is theoretical in nature. Basic definitions and axioms of probability and notions of independence and conditional probability introduced. Focus on random variables, both continuous and discrete, and covers topics of expectation, variance, conditional distributions, conditional expectation and variance, and moment generating functions. Also Central Limit Theorem for sums of random variables. Consists of lectures, recitations, weekly homework, and in-class exams

IEOR E3700 Research Immersion in OR and Data Analytics. 3.00 points.

Prerequisites: IEOR E3608 AND IEOR E3658 AND IEOR E4307; For IEOR undergraduates only.
An overview of active research areas in Operations Research and Data Analytics, and an introduction to the essential components of research studies. This course helps students develop fundamental research skills, including paper reading, problem formulation, problem-solving, scientific writing, and research presentation. Classes are in seminar format, with students analyzing research papers, developing research projects, and presenting research findings

IEOR E3899 Research Training. 0.00 points.

Research training course. Recommended in preparation for laboratory related research

IEOR S3900 UNDERGRAD RESEARCH OR PROJECT. 1.00-3.00 points.

IEOR E3900 UNDERGRAD RESEARCH OR PROJECT. 1.00-3.00 points.

Prerequisites: Approval by a faculty member who agrees to supervise the work.
Independent work involving experiments, computer programming, analytical investigation, or engineering design

IEOR E3999 FIELDWORK. 1.00-2.00 points.

1-1.5 pts. (up to 2 pts. summer only)

Prerequisites: Obtained internship and approval from faculty advisor. Only for IEOR undergraduate students who need relevant work experience as part of their program of study.
Final reports are required. This course may not be taken for pass/fail credit or audited

IEOR E4001 DESIGN/MGT OF PROD/SERV SYSTMS. 3.00 points.

IEOR E4003 CORPORATE FINANCE FOR ENGINEERS. 3.00 points.

Lect: 3.

Prerequisites: Probability theory and linear programming. Required for all undergraduate students majoring in IE, OR:EMS, OR:FE, and OR.
Introduction to the economic evaluation of industrial projects. Economic equivalence and criteria. Deterministic approaches to economic analysis. Multiple projects and constraints. Analysis and choice under risk and uncertainty

IEOR E4004 OPTIMIZATION MODELS AND METHODS. 3.00 points.

Lect: 3.

Prerequisites: Refer to course syllabus.
A graduate course only for MS＆E, IE, and OR students. This is also required for students in the Undergraduate Advanced Track. For students who have not studied linear programming. Some of the main methods used in IEOR applications involving deterministic models: linear programming, the simplex method, nonlinear, integer and dynamic programming

IEOR E4007 OPT MODELS ＆ METHODS FOR FE. 3.00 points.

Lect: 3.

Prerequisites: Linear algebra. This graduate course is only for M.S. Program in Financial Engineering students.
Linear, quadratic, nonlinear, dynamic, and stochastic programming. Some discrete optimization techniques will also be introduced. The theory underlying the various optimization methods is covered. The emphasis is on modeling and the choice of appropriate optimization methods. Applications from financial engineering are discussed

IEOR E4008 COMPUTATION DISCRETE OPT. 3.00 points.

Not offered during 2023-2024 academic year.

Prerequisites: Linear programming, basic probability theory.
Discrete optimization problems. Mathematical techniques and testing strengths and limits in practice on relevant applications. Transportation (travelling salesman and vehicle routing) and matching (online advertisement and school allocation) problems

IEOR E4009 NON-LINEAR OPTIMIZATION. 3.00 points.

Lect.: 2.5.Not offered during 2023-2024 academic year.

Prerequisites: A course on optimization models and methods at the level of IEOR E4004 and a course on linear algebra.
Unconstrained and constrained nonlinear optimization involving continuous functions. Additionally, fundamental concepts such as optimality conditions and convergence, principle focus on practical optimization methods

IEOR E4100 STATISTICS ＆ SIMULATION. 1.50 point.

Lecture 1.5

Prerequisites: Understanding of single- and multivariable calculus.
Probability and simulation. Statistics building on knowledge in probability and simulation. Point and interval estimation, hypothesis testing, and regression. A specialized version of IEOR E4150 for MSE and MSBA students who are exempt from the first half of IEOR E4101. Must obtain waiver for E4101

IEOR E4101 PROBABILITY STAT ＆ SIMULATION. 3.00 points.

Prerequisites: Understanding of single- and multivariable calculus. MSE and MSBA students only.
Basic probability theory, including independence and conditioning, discrete and continuous random variable, law of large numbers, central limit theorem, and stochastic simulation, basic statistics, including point and interval estimation, hypothesis testing, and regression; examples from business applications such as inventory management, medical treatments, and finance. A specialized version of IEOR E4150 for MSE and MSBA students

IEOR E4102 STOCHASTIC MODELING FOR MSE. 3.00 points.

Prerequisites: IEOR E4101
Introduction to stochastic processes and models, with emphasis on applications to engineering and management; random walks, gambler’s ruin problem, Markov chains in both discrete and continuous time, Poisson processes, renewal processes, stopping times, Wald’s equation, binomial lattice model for pricing risky assets, simple option pricing; simulation of simple stochastic processes, Brownian motion, and geometric Brownian motion. A specialized version of IEOR E4106 for MSE students

IEOR S4105E Probability. 3 points.

Fundaments, random variables and distribution functions in one or more dimensions; moments, conditional probabilities, and densities; Laplace transforms and characteristic functions. Infinite sequences of random variables; weak and strong laws of large numbers; central limit theorem. \n

IEOR E4106 STOCHASTIC MODELS. 3.00 points.

Lect: 3.

Prerequisites: (STAT GU4001) STAT W4001 OR IEOR E4150; STAT GU4001 or probability theory or IEOR E4150. This graduate course is only for IE and OR students. Also required for students in the Undergraduate Advanced Track.
Some of the main stochastic models used in engineering and operations research applications: discrete-time Markov chains, Poisson processes, birth and death processes and other continuous Markov chains, renewal reward processes. Applications: queueing, reliability, inventory, and finance

IEOR E4108 SUPPLY CHAIN ANALYTICS. 3.00 points.

Prerequisites: IEOR E3402, IEOR E4000 or instructor’s permission.

Prerequisites: see notes re: points IEOR E3402; or instructor's permission. MS IEOR students only.
Supply chain management, model design of a supply chain network, inventories, stock systems, commonly used inventory models, supply contracts, value of information and information sharing, risk pooling, design for postponement, managing product variety, information technology and supply chain management; international and environmental issues. Note: replaced IEOR E4000 beginning in fall 2018

IEOR E4111 OPERATIONS CONSULTING. 3.00 points.

Prerequisites: (IEOR E3658) and (IEOR E4307) or (STAT GU4001) and Probability and statistics at the level of IEOR E3658 and E4307 or STAT GU4001, and Deterministic Models at the level of IEOR E3608 or IEOR E4004, or instructor permission. For MS-MS&E students only.
Aims to develop and harness the modeling, analytical, and managerial skills of engineering students and apply them to improve the operations of both service and manufacturing firms. Structured as a hands-on laboratory in which students "learn by doing" on real-world consulting projects (October to May). The student teams focus on identifying, modeling, and testing (and sometimes implementing) operational improvements and innovations with high potential to enhance the profitability and/or achieve sustainable competitive advantage for their sponsor companies. The course is targeted toward students planning careers in technical consulting (including operations consulting) and management consulting, or pursuing positions as business analysts in operations, logistics, supply chain and revenue management functions, positions in general management, and future entrepreneurs

IEOR E4150 INTRO-PROBABILITY ＆ STATISTICS. 3.00 points.

Lect: 3.

Prerequisites: Calculus, including multiple integration.
Covers the following topics: fundamentals of probability theory and statistical inference used in engineering and applied science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference: pint and confidence interval estimation, hypothesis tests, linear regression. For IEOR graduate students

IEOR E4177 Think Bigger. 3.00 points.

Innovative solutions to complex problems that are both novel and useful. Focuses on The Think Bigger Innovation Method, uses decision-making theory, cognitive science, and industry practice to facilitate creativity and innovation. Designed to foster new ideas during the beginning of the semester that will then function as the seeds for entrepreneurially minded. Culminates in a final project with presentation of formal and polished pitch of an innovative idea in front of a distinguished panel of successful minds from across the city

IEOR E4199 MSIEOR Quantitative Bootcamp. 0.00 points.

Zero-credit course. Primer on quantitative and mathematical concepts. Required for all incoming MSOR and MSIE students

IEOR E4205 STUDIES IN OPERATIONS RESEARCH. 3.00 points.

IEOR E4206 Intellectual Property for Engineers. 0.00 points.

Many of this century’s biggest business successes have emerged from science-based innovations: Google, next gen semiconductors, cancer therapeutics, DNA sequencing, CRISPR, advanced batteries, and more. For most of these, strong intellectual property is a critical part of the business’ success. This class will provide an overview of how intellectual property (primarily patents, but also trademarks and copyrights) is created, protected, and leveraged by successful startups and large companies alike. In addition to lectures and exercises, there will be many guest speakers from successful venture capital firms, startups, and industry. While legal principles will be addressed, the primary focus is on leveraging intellectual property to create competitive advantage

IEOR E4207 HUMAN FACTORS: PERFORMANCE. 3.00 points.

Lect: 3.

Prerequisites: Refer to course syllabus.
Required for undergraduate students majoring in IE. Sensory and cognitive (brain) processing considerations in the design, development, and operations of systems, products, and tools. User or operator limits and potential in sensing, perceiving decision making, movement coordination, memory, and motivation

IEOR E4208 SEM IN HUMAN FACTORS DESIGN. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4207) or IEOR E4207; IEOR E4207 or instructor’s permission.
An elective for undergraduate students majoring in IE. An in-depth exploration of the application potential of human factor principles for the design of products and processes. Applications to industrial products, tools, layouts, workplaces, and computer displays. Consideration to environmental factors, training and documentation. Term project

IEOR E4210 Supply Chain Management. 3 points.

Lect: 3.Not offered during 2023-2024 academic year.

Prerequisites: IEOR 3402, IEOR 4000 or permission of instructor.

This is a IE elective for undergraduate students majoring in IE. Major issues in supply chain management, including, definition of a supply chain; role of inventory; supply contracts; bullwhip effect and information sharing; vendor-managed inventories and other distribution strategies; third-party logistics providers; managing product variety; information technology and supply chain management; international issues. Emphasis on quantitative models and analysis.

IEOR E4211 APPLIED CONSULTING. 3.00 points.

Prerequisites: (IEOR E3658) and (IEOR E4307) or (IEOR E4150) or (STAT GU4001) and

Basic and advanced techniques in commercial and government consulting. Case studies supported by lectures focused on collecting and analyzing skills, client/market data, client interview techniques, and application of quantitative and qualitative methodologies. Exposure to critical skills on workplan development, interview techniques, presentation deck preparation, costing, and application of analytic techniques to solve complex problems. 

IEOR E4212 Data Analytics ＆ Machine Learning for OR. 3.00 points.

Surveys tools available in Python for getting (web scraping and APIs) and visualizing data (charts and maps). Introduction to analytics through machine learning (ML algorithms, model evaluation, text analytics, network algorithms, deep learning)

IEOR E4307 STATISTICS AND DATA ANALYSIS. 3.00 points.

Lect: 3.

Prerequisites: probability, linear algebra.
Descriptive statistics, central limit theorem, parameter estimation, sufficient statistics, hypothesis testing, regression, logistic regression, goodness-of-fit tests, applications to operations research models

IEOR E4311 Derivatives Marketing ＆ Structuring. 1.50 point.

Points: 1.5 Prerequisites: IEOR E3402, IEOR E4000 or permission of instructor.

Prerequisites: see notes re: points IEOR E3402 OR IEOR S4000; IEOR E3402, IEOR E4000, or instructor's permission.
Covers topics in Accounting, relationships among different elements of financial statements, short- term and long-term financing alternatives, using swaps, cap, and floors to manage interest rate risk, hedging interest risk of corporate finance, using options as cheapeners, structured swaps, accounting treatment of derivatives, cash flow hedging, accrual accounting, hedging issuance of a bond using treasuries, hedging employee stock options, preferred shares and their use in corporate treasury, FX risk and FX translation, commodities hedging, operating lease vs capital lease, credit risk, and cred spread and funding. Note: restricted to IEOR MS students

IEOR E4312 Application of OR ＆ AI Techniques in Marketing. 1.50 point.

1.5 Points Prerequisites: Working knowledge EXCEL and a high-level language such as Python, R, MATLAB, or VBA and an introductory courses in Probability and Statistics.

Prerequisites: see notes re: points Working knowledge of Excel, high level language such as Python, R, MATLAB, or VBA, and intro course in probability and statistics.
Covers working knowledge of quantitative methods and data mining techniques applied to marketing and customer relationship management. Topics include clustering methods, conjoint analysis and customer preferences, forecasting, market share, product life cycle, new product, nearest neighbor, discriminant analysis, decision tree, revenue management, price and advertising elasticity, resource allocation and return on investment (ROI), economic analysis of a network and its formation, and networked markets. Note: restricted to IEOR MS students

IEOR E4399 MSE Quantitative Bootcamp. 0.00 points.

Zero-credit course. Primer on quantitative and mathematical concepts. Required for all incoming MSOR and MSIE students

IEOR E4402 Corporate Finance, Accounting ＆ Investment Banking. 3.00 points.

Prerequisites: Covers primary financial theories and alternative theories underlying Corporate Finance, such as CAPM, Miller Modigliani, Fama French factors, Smart Beta, etc.
Interpret financial statements, build cash flow models, value projects, value companies, and make Corporate Finance decisions. Additional topics include: cost of capital, dividend policy, debt policy, impact of taxes, Shareholder/Debtholder agency costs, dual-class shares, using option pricing theory to analyze management behavior, investment banking activities, including equity underwriting, syndicated lending, venture capital, private equity investing and private equity secondaries. Application of theory in real-world situations: analyzing financial activities of companies such as General Electric, Google, Snapchat, Spotify, and Tesla

IEOR E4403 QUANTITATIVE CORPORATE FINANCE. 3.00 points.

Lect: 3.

Prerequisites: Probability theory and linear programming
Required for students in the Undergraduate Advanced Track. Key measures and analytical tools to assess the financial performance of a firm and perform the economic evaluation of industrial projects. Deterministic mathematical programming models for capital budgeting. Concepts in utility theory, game theory and real options analysis

IEOR E4404 SIMULATION. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E3658) and (IEOR E4307) or (STAT GU4001) and IEOR E3106 OR IEOR E4106; IEOR E3658 and IEOR E4307 or STAT GU4001, computer programming. Required for all undergraduate students majoring in IE, OR:EMS, OR:FE, and OR. Also required for MSOR.
Corequisites: IEOR E3106,IEOR E4106
Generation of random numbers from given distributions; variance reduction; statistical output analysis; introduction to simulation languages; application to financial, telecommunications, computer, and production systems. Graduate students must register for 3 points. Undergraduate students must register for 4 points. Note: Students who have taken IEOR E4703 Monte Carlo simulation may not register for this course for credit. Recitation section required

IEOR E4405 PRODUCTION SCHEDULING. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E3608) and (IEOR E3658) and IEOR E3608 AND IEOR E3658; Computer programming.
Required for undergraduate students majoring in IE and OR. Job shop scheduling: parallel machines, machines in series; arbitrary job shops. Algorithms, complexity, and worst-case analysis. Effects of randomness: machine breakdowns, random processing time. Term project

IEOR E4406 Facilities Location, Routing and Network Design. 3 points.

Lect: 3.Not offered during 2023-2024 academic year.

Prerequisites: IEOR E3608 or IEOR E4004

Facility location problems in application areas such as telecommunications networks, product distribution systems and emergency services. Emphasis on applications, algorithmic approaches, routing and network design problems.

IEOR E4407 GAME THEOR MODELS OF OPERATION. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4004) or (IEOR E3608) and (IEOR E4106) or (IEOR E3106) and IEOR E4004 or IEOR E3608, IEOR E4106 or IEOR E3106, familiarity with differential equations and computer programming; or instructor’s permission.
Required for undergraduate students majoring in OR:FE and OR. A mathematically rigorous study of game theory and auctions, and their application to operations management. Topics include introductory game theory, private value auction, revenue equivalence, mechanism design, optimal auction, multiple-unit auctions, combinatorial auctions, incentives, and supply chain coordination with contracts. No previous knowledge of game theory is required

IEOR E4408 RESOURCE ALLOCATION. 3.00 points.

Lect: 3.Not offered during 2023-2024 academic year.

Prerequisites: (IEOR E3608) or (IEOR E4004) and IEOR E3608 OR IEOR E4004; or basic knowledge of nonlinear and integer programming.
Overview of resource allocation models. Single resource allocation with concave returns; equitable resource allocation; lexicographic minmax/maxmin optimization; extensions to substitutable resources; multiperiod resource allocation; equitable allocation in multi-commodity network flow models; equitable content distribution in networks; equitable resource allocation with discrete decision variables

IEOR E4412 QUALITY CONTROL AND MANAGEMENT. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E3658) and (STAT GU4001) IEOR E3658; Working knowledge of statistics.
Required for undergraduate students majoring in IE. Statistical methods for quality control and improvement: graphical methods, introduction to experimental design and reliability engineering and the relationships between quality and productivity. Contemporary methods used by manufacturing and service organizations in product and process design, production and delivery of products and services

IEOR E4418 TRANSPORTATION ANALYTICS ＆ LOGISTICS. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E3608 or IEOR E4404 or IEOR E4007 or CSOR W4231 or CSOR W4246) and (IEOR E3106 or IEOR E4307 or SIEO W3600 or IEOR E4100 or IEOR E4101 or IEOR E4150 or STAT GR5701 or STAT GR5703) or IEOR E3608 or IEOR E4404 or permission of instructor.
Transportation, primarily focused on the movement of people, and logistics, primarily focused on the movement of goods, are two of the most fundamental challenges to modern society. To address many problems in these areas, a wide array of mathematical models and analytics tools have been developed. This class will introduce many of the foundational tools used in transportation and logistics problems, relying on ideas from linear optimization, integer optimization, stochastic processes, statistics, and simulation. We will address problems such as optimizing the routes of cars and delivery trucks, positioning emergency vehicles, and controlling traffic behavior. Moreover, we will discuss modern issues such as bicycle sharing, on-demand car and delivery services, humanitarian logistics, and autonomous vehicles. Concepts will be reinforced with technical content as well as real-world data and examples

IEOR E4500 APPLICATIONS PROGRAMMNG FOR FE. 3.00 points.

Lect: 3.

Prerequisites: Computer programming or instructor’s approval. Required for undergraduate students majoring in OR:FE.
We will take a hands-on approach to developing computer applications for Financial Engineering. Special focus will be placed on high-performance numerical applications that interact with a graphical interface. In the course of developing such applications we will learn how to create DLLs, how to integrate VBA with C/C programs, and how to write multithreaded programs. Examples of problems settings that we consider include simulation of stock price evolution, tracking, evaluation and optimization of a stock portfolio; optimal trade execution. In the course of developing these applications, we review topics of interest to OR:FE in a holistic fashion

IEOR E4501 TOOLS FOR ANALYTICS. 3.00 points.

MS IEOR students only. Introduction programming in Python, tools with the programmer's ecosystem. Python, Data Analysis tools in Python (NumPy, pandas, bokeh), GIT, Bash, SQL, VIM, Linux/Debia, SSH

IEOR E4502 Python for Analytics. 0.00 points.

Zero-credit course. Primer on Python for analytics concepts. Required for MSBA students

IEOR E4505 OPERATION RES IN PUBLIC POLICY. 3.00 points.

Prerequisites: (IEOR E3608) or (IEOR E4004) and (IEOR E3106) or (IEOR E4106) IEOR E3608: Introduction to Mathematical Programming or IEOR E4004: Introduction to Operations Research: Deterministic Models, IEOR E3106 or IEOR E4106: Introduction to Operations Research: Stochastic Models.
Aims to give the student a broad overview of the role of Operations Research in public policy. The specific areas covered include voting theory, apportionment, deployment of emergency units, location of hazardous facilities, health care, organ allocation, management of natural resources, energy policy, and aviation security. Draws on a variety techniques such as linear and integer programming, statistical and probabilistic methods, decision analysis, risk analysis, and analysis and control of dynamic systems

IEOR E4506 DESIGN DIGITAL OPERATING MODELS. 3.00 points.

IEOR students only. Understand digital businesses, apply scientific, engineering thinking to digital economy. Data-driven digital strategies and operating models. Sectors: ecommerce, advertising technology, and marketing technology. Automation of the marketing, sales, and advertising functions. Algorithms, patents, and business models. Business side of the digital ecosytem and the digital economy

IEOR E4507 HEALTHCARE OPERATIONS MGT. 3.00 points.

Not offered during 2023-2024 academic year.

Prerequisites: (IEOR E3608) and (IEOR E3658) and (IEOR E4307) For senior undergraduate Engineering students: SIEO W3600: Introduction to Probability and Statistics and IEOR E3608: Introduction to Mathematical Programming; For Engineering graduate students (MS or PhD): Probability and Statistics at the level of SIEO W4150, and Deterministic Models at the level of IEOR E4004. For Healthcare Management students: P8529: Analytic Methods for Health Services Management.
Prerequisite(s): for senior undergraduate Engineering students: IEOR E3608, E3658, and E4307; for Engineering graduate students (M.S. or Ph.D.): Probability and statistics at the level of IEOR E4150, and deterministic models at the level of IEOR E4004; for healthcare management students: P8529 Analytical methods for health services management. Develops modeling, analytical, and managerial skills of engineering and health care management students. Enables students to master an array of fundamental operations management tools adapted to the management of health care systems. Through real-world business cases, students learn to identify, model, and analyze operational improvements and innovations in a range of health care contexts

IEOR E4510 PROJECT MANAGEMENT. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4004) or (IEOR E3608) IEOR E4004 OR IEOR E3608
Management of complex projects and the tools that are available to assist managers with such projects. Topics include project selection, project teams and organizational issues, project monitoring and control, project risk management, project resource management, and managing multiple projects

IEOR E4511 Industry Projects in Analytics ＆ Operations Research. 3.00 points.

Teams of students work on real-world projects in analytics. Focus on three aspects of analytics: identifying client analytical requirements; assembling, cleaning and organizing data; identifying and implementing analytical techniques (e.g., statistics and/or machine learning); and delivering results in a client-friendly format. Each project has a defined goal and pre-identified data to analyze in one semester. Client facing class. Class requires 10 hours of time per week and possible client visits on Fridays

IEOR E4520 APPLIED SYSTEMS ENGINEERING. 3.00 points.

Lect: 3.

Prerequisites: B.S. in engineering or applied sciences; professional experience recommended; calculus, probability and statistics, linear algebra.
Introduction to fundamental methods used in systems engineering. Rigorous process that translates customer needs into a structured set of specific requirements; synthesizes a system architecture that satisfies those requirements and allocates them in a physical system, meeting cost, schedule, and performance objectives throughout the product life-cycle. Sophisticated modeling of requirements optimization and dependencies, risk management, probabilistic scenario scheduling, verification matrices, and systems-of-systems constructs are synthesized to define the meta-workflow at the top of every major engineering project

IEOR E4521 SYSTEM ENGI TOOLS/METHODS. 3.00 points.

Prerequisites: B.S. in engineering or applied sciences; probability and statistics, optimization, linear algebra, and basic economics.
Applications of SE tools and methods in various settings. Encompasses modern complex system development environments, including aerospace and defense, transportation, energy, communications, and modern software-intensive systems

IEOR E4522 PYTHON FOR OPERATIONS RESEARCH. 1.50 point.

Lect: 1.5.Not offered during 2023-2024 academic year.

IEOR Students Only; Priority to MSOR Students. Introduction to programming in Python, providing a working knowledge of how to use Python to extract knowledge and information from data. Overview of Python libraries for data analysis. Fundamental course for MSOR students in order to engage in higher level analytics courses.

IEOR E4523 DATA ANALYTICS. 3.00 points.

Lect: 3.

Prerequisites: IEOR E4522 OR IEOR E4501
Corequisites: IEOR E4501
IEOR students only; priority to MSBA students. Survey tools available in Python for getting, cleaning, and analyzing data. Obtain data from files (csv, html, json, xml) and databases (Mysql, PostgreSQL, NoSQL), cover the rudiments of data cleaning, and examine data analysis, machine learning, and data visualization packages (NumPy, pandas, Scikit-lern, bokeh) available in Python. Brief overview of natural language processing, network analysis, and big data tools available in Python. Contains a group project component that will require students to gather, store, and analyze a data set of their choosing

IEOR E4524 ANALYTICS IN PRACTICE. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4523) and IEOR E4501
MSBA students only. Groups of students will work on real world projects in analytics, focusing on three aspects: identifying client analytical requirements; assembling, cleaning, and organizing data; identifying and implementing analytical techniques (statistics, OR, machine learning); and delivering results in a client-friendly format. Each project has a well-defined goal, comes with sources of data preidentified, and has been structured so that it can be completed in one semester. Client-facing class with numerous on-site client visits; students should keep Fridays clear for this purpose

IEOR E4525 MACHINE LEARNING FE ＆ OPR. 3.00 points.

Prerequisites: Optimization, applied probability, statistics, and knowledge of or experience in computer programming.
MS IEOR students only. Introduction to machine learning, practical use of ML algorithms and applications to financial engineering and operations. Supervised learning: regression, classification, resampling methods, regularization, support vector machines (SVMs), and deep learning. Unsupervised learning: dimensionality reduction, matrix decomposition, and clustering algorithms

IEOR E4526 ANALYTICS ON THE CLOUD. 3.00 points.

Prerequisites: IEOR E4501 and IEOR E4523 IEOR E4501 AND IEOR E4523
To introduce students to programming issues around working with clouds for data analytics. Class will learn how to work with infrastructure of cloud platforms, and discussion about distributed computing, focus of course is on programming. Topics covered include MapReduce, parallelism, rewriting of algorithms (statistical, OR, and machine learning) for the cloud, and basics of porting applications so that they run on the cloud

IEOR E4530 TOPICS IN OPERATIONS RESEARCH. 3.00 points.

Prerequisites: A course in probability and statistics 4101/4150 or equivalent, basic python programming.
This course will cover the basics of game theory and market design, with a focus on how AI and optimization enables large-scale game solving and markets. We will cover the core ideas behind recent superhuman AIs for games such as Poker. Then, we will discuss how AI and game theory ideas are used in marketplaces such as internet advertising, fair course seat allocation, and spectrum reallocation. This is intended to be an advanced MS level and senior undergraduate course for students in Operations Research and Financial Engineering

IEOR E4532 Visualization and Storytelling with Data. 1.50 point.

Data visualization and how to build a story with data. Using complex data or statistics to communicate results effectively. Learn to present analysis and results conscisely and effectively

IEOR E4533 Performance, Objectives, ＆ Results Using Data Analytics. 1.50 point.

OKR framework and different variations. Measurement techniques (A/B testing, validation, correlation, etc.) Identifying what to measure in product experience and business initiatives. Data-driven decision making

IEOR E4534 Applied Analytics: from Data to Decisions. 3.00 points.

Applied Analytics focus querying and transforming data with SQL, defining and visualizing metrics, measuring impact of products / processes. Tools and techniques to convert raw data to business decisions, statistical analysis. Be able to apply these techniques to real-world datasets

IEOR E4540 DATA MINING. 3.00 points.

Prerequisites: Linear Algebra, Calculus, Probability, and some basic programming.
Course covers major statistical learning methods for data mining under both supervised and unsupervised settings. Topics covered include linear regression and classification, model selection and regularization, tree-based methods, support vector machines, and unsupervised learning. Students learn about principles underlying each method, how to determine which methods are most suited to applied settings, concepts behind model fitting and parameter tuning, and how to apply methods in practice and assess their performance. Emphasizes roles of statistical modeling and optimization in data mining

IEOR E4544 Statistical Methods for Analytics. 3.00 points.

Focus on advanced statistical techniques for a career in data science or business analytics. Covers the use of writing probabilistic models for data-generating processes, using Bayesian Methods/MCMC to solve such problems. Emphasizes problem identification and general problem-solving tools. Special Topics: Survival Analysis, Missing Data, Robust Statistics, Sequential Analysis, Multiple Testing. Assignments are case-based

IEOR E4545 Causal Analysis for Data Analytics and OR. 3.00 points.

Survey of different approaches to causal inference with an emphasis on applications. Counterfactuals, Causal Structural Models and Graphical Models. G-formula, IP weighting, and g-estimation. Backdoor and Frontdoor adjustment, introduction to the do-calculus. Regression discontinuity, Instrumental Variables, Difference-in-Difference, Synthetic Control. Advanced Topics: Causal Survival Analysis, Time-Varying Treatments, Competing Events. Emphasis on problem-solving and working with data

IEOR E4550 ENTREPRENEURIAL BUS CREA-ENGIN. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E2261) IEOR E2261
Required for undergraduate students majoring in OR:EMS. Introduces the basic concepts and methodologies that are used by the nonengineering part of the world in creating, funding, investing in, relating to, and operating entrepreneurial ventures. The first half of the course focuses on the underpinning principles and skills required in recognizing, analyzing, evaluating, and nurturing a business idea. The second half focuses on basic legal knowledge necessary in creating a business entity, defending your business assets, and in promoting effective interaction with other individuals and organizations

IEOR E4555 DESIGN/AGILE PROJ MGMT ENG LAB. 3.00 points.

Lect: 3.Not offered during 2023-2024 academic year.

Intensive, team-, and project-based seminar covering multidisciplinary approach to evidence-based product design; agile project planning and execution; rapid MVP prototyping; and launch strategy formulation and implementation. Focuses on practical use of design thinking, design studio, and iterative design sprint methodologies. Systematic approaches to Lean User Research, User Experience (UX), and User Interface (UI) design and deployment are integral components of course curriculum. Mix of startup and enterprise projects, including application drive, data-driven, or combination of both. Teams are fully supported in devising prototypes and actualizing proposed solutions

IEOR E4560 THE LEAN LAUNCH PAD. 3.00 points.

IEOR E4561 LAUNCH YOUR STARTUP: TECH. 3.00 points.

Tools and knowledge to develop a comprehensive new venture that is scalable, repeatable, and capital efficient. Covers customer discovery, market sizing, pricing, competition, distribution, funding, developing a minimal viable product, and other facets of creating new ventures. A company blueprint and final investor pitch are deliverables

IEOR E4562 Innovate Using Design Thinking. 3.00 points.

How Design Thinking can enhance innovation activities, market impact, value creation, and speed. Topics include: conceptual and practical understanding of design thinking, creative solutions, develop robust practices to lead interdisciplinary teams. Course aims to strengthen individuals and collaborative capabilities to identify customer needs, indirect and qualitative research, create concept hypotheses, develop prototype, defined opportunities into actionable innovation possibilities, and recommendations for client organizations

IEOR E4563 Technology Breakthroughs. 1.50 point.

Technological breakthroughs driven change, disruption, and transformation of the business landscape/society. Course covers overview of deep learning and neural networks; AI and robotics; imaging and vision; photonics; blockchain; smart/digital cities; and the application of these technologies for creating new products and services

IEOR E4570 TOPICS IN OPERATIONS RESEARCH. 1.50 point.

1.5 points

Prerequisites: IEOR E4004 AND IEOR E4106 AND IEOR E4150; or instructor's permission.
This course is designed as an introductory exposure to entrepreneurial concepts and practical skills for engineering students (and others) who wish to explore entrepreneurship conceptually or as a future endeavor in their careers. The class will be a mix of lecture, discussion, team-building and in-the-field workshopping of concepts we cover

IEOR E4571 TOPICS IN OPERATIONS RESEARCH. 3.00 points.

Prerequisites: IEOR E4004 AND IEOR E4106 AND IEOR E4150; IEOR E4150, IEOR E4004, and IEOR E4106, or instructor's permission.
The last decade of 20 th century witnessed a rapid convergence of three C’s: Communications, Computers, and Consumer Electronics. This convergence has given us the Internet, smart phones, and an abundance of data with Data Science playing a major role in analyzing these data and providing predictive analytics that lead to actionable items in many fields and businesses. Finance is a field with a large amount of information and data that can utilize the skills of Data Scientists, however, to be effective in this field a data scientist, in addition to analytic knowledge, should also be knowledgeable of the working, instruments, and conventions of financial markets that range from Foreign Exchange to Equities, Bonds, Commodities, Cryptocurrencies and host of other asset classes. The objective of this course is to provide Data Science students with a working knowledge of major areas of finance that could help them in finding a position in the Financial Industry. The wide range of topics covered in this course besides expanding the range of positions where students could be a fit, it gives them more flexibility in their job search. The course will also be of value to them in managing their own finances in the future

IEOR E4572 TOPICS IN OPERATIONS RESEARCH. 3.00 points.

Lect: 3.,Points: 1.5

Prerequisites: IEOR E4004 AND IEOR E4106 AND IEOR E4150; or instructor's permission.
Each offering of this course is devoted to a particular sector of Operations Research and its contemporary research, practice, and approaches. If topics are different, then course can be taken more than once for credit

IEOR E4573 TOPICS IN OR. 3.00 points.

Points: 1.5

Prerequisites: IEOR E4004 AND IEOR E4106 AND IEOR E4150; IEOR E4150, IEOR E4004, and IEOR E4106, or instructor's permission.
Each offering of this course is devoted to a particular sector of Operations Research and its contemporary research, practice, and approaches. If topics are different, then course can be taken more than once for credit

IEOR E4574 TOPICS IN OR. 3.00 points.

Prerequisites: IEOR E4004 AND IEOR E4106 AND IEOR E4150; IEOR E4150, IEOR E4004, and IEOR E4106, or instructor's permission.
Each offering of this course is devoted to a particular sector of Operations Research and its contemporary research, practice, and approaches. If topics are different, then course can be taken more than once for credit

IEOR E4575 TOPICS IN OPERATIONS RESEARCH. 3.00 points.

Note to students: 1.5 credits Note to students re: pre-requisites: Probability and statistics, Basic optimization (e.g., familiarity with linear and convex optimization, gradient descent, basic algorithm design constructs), familiarity with Programming in python (or experience with programming in other languages like C/C++/Matlab and willingness to learn python). Knowledge of machine learning is not required, but some basic familiarity may help.

Prerequisites: IEOR E4004 AND IEOR E4106 AND IEOR E4150; or instructor's permission.
Each offering of this course is devoted to a particular sector of Operations Research and its contemporary research, practice, and approaches. If topics are different, then course can be taken more than once for credit

IEOR E4576 TOPICS IN OPERATIONS RESEARCH. 3.00 points.

1.5 pts

Prerequisites: IEOR E4004 AND IEOR E4106 AND IEOR E4150; IEOR E4150, IEOR E4004, and IEOR E4106, or instructor's permission.
This Columbia University course offers a project-based learning experience focused on systematic quantitative investment. It covers the full data science workflow, from concept to performance evaluation. Students will engage in a real-time financial forecasting competition, using open-source financial and alternative data, to make and present investment decisions. Ideally, this course suits students aspiring to careers as quants or data scientists in the financial sector

IEOR E4577 TOPICS IN OPERATIONS RESEARCH. 1.50 point.

Points: 1.5

Prerequisites: IEOR E4004 AND IEOR E4106 AND IEOR E4150; IEOR E4150, IEOR E4004, and IEOR E4106, or instructor's permission
The course focuses on a PRACTICAL study of how to quantify ＆ predict RISK in organizations by using learnings from: Regression analysis; Monte Carlo simulation; Factor analysis; Cohort analysis; Cluster analysis; Time series analysis; Sentiment analysis. Expectation is that incoming students should have a basic understanding of such concepts and statistics. The course will offer meeting ＆ listening to CXO's ＆ top executives from companies who have implemented robust AI ＆ Applied Risk solutions to solve real-world problems in their own industries. It will give students a great opportunity to learn practical applications of predictive analytics to solve real business problems

IEOR E4578 TOPICS IN OPERATION RESEARCH. 3.00 points.

Prerequisites: IEOR E4004 AND IEOR E4106 AND IEOR E4150; IEOR E4150, IEOR E4004, and IEOR E4106, or instructor's permission.
A project-based course in Forecasting, predicting a time series into the future, to prepare students for real-world applications including articulating the business case, value creation, problem statement, and the iterative development of solutions including building a data pipeline, exploration, modeling, and visualizations. The course will use Statistical methods, Machine Learning, and Deep Learning with Transformer-based methods to predict a time series. It will use nuggets of signal processing to augment Machine Learning models to characterize and filter orders of dynamics in the time series data

IEOR E4579 TOPICS IN OR. 3.00 points.

Prerequisites: IEOR E4004 AND IEOR E4106 AND IEOR E4150; IEOR E4150, IEOR E4004, and IEOR E4106, or instructor's permission.
In this course, you'll leverage student engagement data to create a photo and text recommendation app similar to Instagram/Twitter. This app will utilize AI-generated photos and text and require you to recommend a feed from over 500,000 pieces of AI generated content. We'll explore various techniques to achieve this, including, but not limited to: Candidate Generation (Collaborative filtering, Trending, Cold start, N-tower neural network models, Cross-attention teachers, Distillation, Transfer learning, Random graph walking, Reverse indexes, LLMs as embedding), Filtering (Small online models, Caching, Deduplication, Policy), Prediction/Bidding (User logged activity based prediction (time-series), Multi-gate mixture of experts (MMOE), Regularization, Offline/Online evaluation (NDCG, p@k, r@k), Boosted Trees, Value Based Bidding), Ranking (Re-ranking, Ordering, Diversity, Enrich/Metadata/Personalization, Value Functions), Misc (Data Privacy and AI Ethics, Creator Based Models, Declared, Explicit and implicit topics, Explore/Exploit, Interpret/Understand/Context/Intention). These concepts are applicable to various recommendation systems, from e-commerce to travel to social media to financial modeling. The instructor's experience at Uber Eats, Facebook, Instagram, and Google will provide valuable insights into real-world use cases

IEOR E4598 Business Analytics Practitioners Seminar. 0.00 points.

The IEOR Department hosts Business Analytics Practitioners Seminars throughout the Fall and Spring semesters. There are 5-6 seminars per semester, for a total of 10-12 seminars a year. These seminars feature a variety of academics and professionals in the field of Business Analytics; they are meant to educate and expose students to the latest research and practice. First year BA students are required to attend all BA seminars during their first year as well as submit journals about the material

IEOR E4599 MSBA Quantitative Bootcamp. 0.00 points.

Primer on quantitative and mathematical concepts. Required for all incoming MSBA students

IEOR E4600 APPLIED INTEGER PROGRAMMING. 3.00 points.

Lect: 3.

Prerequisites: Linear programming, linear algebra, and computer programming.

This course is required for undergraduate students majoring in OR. This course covers applications of mathematical programming techniques, especially integer programming, with emphasis on software implementation. This course also covers topics of modeling and solution of problems in supply chain, logistics, routing. Particular emphasis is placed on optimization modeling systems, such as AMPL and OPL and state-of-the-art solvers.

IEOR E4601 DYNAMIC PRICING/REVENUE MGMT. 3.00 points.

Lect: 3.

Prerequisites: (STAT GU4001) and (IEOR E4004) IEOR E4004 AND STAT W4001
Focus on capacity allocation, dynamic pricing and revenue management. Perishable and/or limited product and pricing implications. Applications to various industries including service, airlines, hotel, resource rentals, etc

IEOR E4602 QUANTITATIVE RISK MANAGEMENT. 3.00 points.

Lect: 3.

Prerequisites: (STAT GU4001) and (IEOR E4106) IEOR E4106 AND STAT W4001
Risk management models and tools; measure risk using statistical and stochastic methods, hedging and diversification. Examples include insurance risk, financial risk, and operational risk. Topics covered include VaR, estimating rare events, extreme value analysis, time series estimation of extremal events; axioms of risk measures, hedging using financial options, credit risk modeling, and various insurance risk models

IEOR E4611 DECISION MODELS AND APPLIC. 3.00 points.

Lect: 3.Not offered during 2023-2024 academic year.

Prerequisites: (IEOR E3608) and (IEOR E4004) or (IEOR E3658) and (IEOR E4307) and (STAT GU4001) or SIEO W3600 or IEOR E4150: Introduction to Probability and Statistics or equivalent and IEOR E3608: Introduction to Mathematical Programming or IEOR E4004: Introduction to Operations Research: Deterministic Models or equivalent, or instructor permission.
Corequisites: IEOR E4404

Students are introduced to deterministic and stochastic decision tools used by leading corporations and applied researchers, and apply these software packages to complex, real-world problems in engineering and finance. Building on a basic theoretical understanding of optimization, simulation and game theory obtained in prerequisite classes, students master commercial decision modeling programs such as Premium Solver Professional (linear, integer and non-linear optimization), TreePlan (decision-trees), Crystal Ball (simulation), and OptQuest (optimization under uncertainty). Students are also welcome to complete most modeling assignments with Matlab. After students have mastered the course software, its limitations and the frameworks for applying it, they work in small teams to address (as a mid-term project) one large-scale deterministic project and (as an end-of-semester project) one similarly-complex stochastic problem. While addressing their first projects, students learn effective presentation and project reporting skills, suitable for communicating with CFOs and CEOs. Students present their project analyses to a small panel of industry experts and executives. Throughout the course, the importance of outside-the-model considerations, model limitations and sources of modeling error are stressed, and general frameworks for approaching particular problem types are developed.

IEOR E4615 SERVICE ENGINEERING. 3.00 points.

Lect: 3.Not offered during 2023-2024 academic year.

Prerequisites: (SIEO W3600) and (IEOR E3106) or (IEOR E4106) or Introductory courses in probability and statistics such as IEOR E3658 and E4307, and introductory courses in stochastic processes such as IEOR E3106 or IEOR E4106.
Focus on service systems viewed as stochastic networks, exploiting the theoretical framework of queueing theory. Includes multidisciplinary perspectives involving Statistics, Psychology, and Marketing. Significant emphasis on data analysis, exploiting data from banks, hospitals, and call centers to demonstrate the use of decision support tools. Analytical models, flow models of service networks, Little’s law, measuring methods in face-to-face and computerized systems, forecasting methods, stability of service systems, operational quality of service, economies of scale, staffing, complex service networks, skill-based routing

IEOR E4620 PRICING MODELS FOR FIN ENGIN. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4700) IEOR E4700
Required for undergraduate students majoring in OR:FE. Characteristics of commodities or credit derivatives. Case study and pricing of structures and products. Topics covered include swaps, credit derivatives, single tranche CDO, hedging, convertible arbitrage, FX, leverage leases, debt markets, and commodities

IEOR E4630 ASSET ALLOCATION. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4700) IEOR E4700
Models for pricing and hedging equity, fixed-income, credit-derivative securities, standard tools for hedging and risk management, models and theoretical foundations for pricing equity options (standard European, American equity options, Asian options), standard Black-Scholes model (with multiasset extension), asset allocation, portfolio optimization, investments over longtime horizons, and pricing of fixed-income derivatives (Ho-Lee, Black-Derman-Toy, Heath-Jarrow-Morton interest rate model)

IEOR E4650 BUSINESS ANALYTICS. 3.00 points.

Prerequisites: IEOR E4150 AND STAT W4001
This course focuses on how to identify, evaluate, and capture business analytic opportunities that create value. The course covers basic analytic methods alongside case studies on organizations that successfully deployed these techniques. The first part of the course is on using data to develop insights and predictive capabilities with machine learning techniques. The second part focuses on the use of A/B testing, causal inference, ethics, and optimization to support decision-making

IEOR E4651 DATA MINING. 3.00 points.

IEOR E4700 INTRO TO FINANCIAL ENGINEERING. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E3106) or (IEOR E4106) IEOR E4106 OR IEOR E3106
Prerequisite(s): IEOR E4106 or E3106. Required for undergraduate students majoring in OR:FE. Introduction to investment and financial instruments via portfolio theory and derivative securities, using basic operations research/engineering methodology. Portfolio theory, arbitrage; Markowitz model, market equilibrium, and the capital asset pricing model. General models for asset price fluctuations in discrete and continuous time. Elementary introduction to Brownian motion and geometric Brownian motion. Option theory; Black-Scholes equation and call option formula. Computational methods such as Monte Carlo simulation

IEOR E4701 STOCHASTIC MODELS FOR FIN ENG. 3.00 points.

Lect: 3.

Prerequisites: (STAT GU4001) STAT W4001
This graduate course is only for M.S. Program in Financial Engineering students, offered during the summer session. Review of elements of probability theory, Poisson processes, exponential distribution, renewal theory, Wald’s equation. Introduction to discrete-time Markov chains and applications to queueing theory, inventory models, branching processes

IEOR E4703 MONTE CARLO SIMULATION METHODS. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4701) IEOR E4701
This graduate course is only for M.S. Program in Financial Engineering students. Multivariate random number generation, bootstrapping, Monte Carlo simulation, efficiency improvement techniques. Simulation output analysis, Markov-chain Monte Carlo. Applications to financial engineering. Introduction to financial engineering simulation software and exposure to modeling with real financial data. Note: Students who have taken IEOR E4404 Simulation may not register for this course for credit

IEOR E4705 STUDIES IN OPERATION RESEARCH. 3.00 points.

IEOR E4706 FOUNDATIONS FR FINANCIAL ENGIN. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4701) and (IEOR E4702) and IEOR E4701 AND IEOR E4702; Linear algebra
This graduate course is only for M.S. Program in Financial Engineering students, offered during the summer session. Discrete-time models of equity, bond, credit, and foreign-exchange markets. Introduction to derivative markets. Pricing and hedging of derivative securities. Complete and incomplete markets. Introduction to portfolio optimization and the capital asset pricing model

IEOR E4707 FE CONTINUOUS TIME MODELS. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4701) IEOR E4701
This graduate course is only for MS program in FE students. Modeling, analysis, and computation of derivative securities. Applications of stochastic calculus and stochastic differential equations. Numerical techniques: finite-difference, binomial method, and Monte Carlo

IEOR E4709 STATISTICAL ANALYSIS AND TIME SERIES. 3.00 points.

Lect: 3.

Prerequisites: IEOR E4702; IEOR E4706 AND IEOR E4707; Probability
Corequisites: IEOR E4706,IEOR E4702
This graduate course is only for M.S. Program in Financial Engineering students. Empirical analysis of asset prices: heavy tails, test of the predictability of stock returns. Financial time series: ARMA, stochastic volatility, and GARCH models. Regression models: linear regression and test of CAPM, non-linear regression and fitting of term structures

IEOR E4710 TERM STRUCTURE MODELING. 3.00 points.

Lect: 3.Not offered during 2023-2024 academic year.

Prerequisites: (IEOR E4706) and (IEOR E4707) and IEOR E4706 AND IEOR E4707; Computer programming.
Interest rate models and numerical techniques for pricing and hedging interest rate contracts and fixed income securities

IEOR E4711 GLOBAL CAPITAL MARKETS. 3.00 points.

Prerequisites: Refer to course syllabus.
An introduction to capital markets and investments providing an overview of financial markets and tools for asset valuation. Topics covered include the pricing of fixed-income securities (treasury markets, interest rate swaps futures, etc.), discussions on topics in credit, foreign exchange, sovereign ad securitized markets—private equity and hedge funds, etc

IEOR E4718 INTRO-IMPLIED VOLATILITY SMILE. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4706) and IEOR E4706; Knowledge of derivatives valuation models.
During the past 15 years the behavior of market options prices have shown systematic deviations from the classic Black-Scholes model. Examines the empirical behavior of implied volatilities, in particular the volatility smile that now characterizes most markets, the mathematics and intuition behind new models that can account for the smile, and their consequences for hedging and valuation

IEOR E4720 TOPICS IN QUANT FINANCE. 3.00 points.

Prerequisites: (IEOR E4700) and IEOR E4700; Additional prerequisites will be announced depending on offering
Selected topics of interest in the area of quantitative finance. Offerings vary each year; some topics include energy derivatives, experimental finance, foreign exchange and related derivative instruments, inflation derivatives, hedge fund management, modeling equity derivatives in Java, mortgage-backed securities, numerical solutions of partial differential equations, quantitative portfolio management, risk management, trade and technology in financial markets

IEOR E4721 TOPICS IN QUANT FINANCE. 1.50 point.

Prerequisites: IEOR E4700; Additional prerequisites will be announced depending on offering
Selected topics of interest in the area of quantitative finance. Offerings vary each year; some topics include energy derivatives, experimental finance, foreign exchange and related derivative instruments, inflation derivatives, hedge fund management, modeling equity derivatives in Java, mortgage-backed securities, numerical solutions of partial differential equations, quantitative portfolio management, risk management, trade and technology in financial markets

IEOR E4722 TOPICS IN QUANT FINANCE. 3.00 points.

Prerequisites: IEOR E4707 IEOR E4700; Additional prerequisites announced depending on offering.
Stochastic control has broad applications in almost every walk of life, including finance, revenue management, energy, health care and robotics. Classical, model-based stochastic control theory assumes that the system dynamics and reward functions are known and given, whereas modern, model-free stochastic control problems call for reinforcement learning to learn optimal policies in an unknown environment. This course covers model-based stochastic control and model-free reinforcement learning, both in continuous time with continuous state space and possibly continuous control (action) space. It includes the following topics: Shortest path problem, calculus of variations and optimal control; formulation of stochastic control; maximum principle and backward stochastic differential equations; dynamic programming and Hamilton-Jacobi-Bellman (HJB) equation; linear-quadratic control and Riccati equations; applications in high-frequency trading; exploration versus exploitation in reinforcement learning; policy evaluation and martingale characterization; policy gradient; q-learning; applications in diffusion models for generative AI

IEOR E4723 TOPICS IN QUANTATIVE FINANCE. 1.50 point.

Course Points: 1.5

Prerequisites: IEOR E4700; Additional Prerequisites will be announced depending on offering
ESG (Environmental, Social and Corporate Governance) Finance is a rapidly growing area of Investment Management – and Finance more broadly – that has received a lot of attention in the past several years from the investor community, financial regulatory agencies, and the general public alike. This course provides an introduction to ESG Finance from a financial engineer’s perspective. This course also discusses proliferation of newly available data sources and the associated quantitative techniques necessary to process those. A major component of this course is a discussion of Climate Risk, an area of particular focus due to its increasing general importance. The course includes an overview of both recent research and the evolving regulatory landscape in the climate risk space. An in-depth discussion of financial impact assessment of various climate risk-driven scenarios (climate risk stress testing) concludes the course

IEOR E4724 TOPICS IN QUANTATIVE FINANCE. 3.00 points.

Prerequisites: IEOR E4700; Additional prerequisites will be announced depending on offering
In this course, we will cover the basics of mathematical modeling of interest rates and credit derivatives. In the first part, we will cover basic interest rate derivatives, the Heath-Jarrow-Morton (HJM) framework, classic short rate models (for both interest rates and default intensities), and the numerical techniques used in practice for their calibration. In the second part, we will cover the basics of single-name derivatives modeling, and we will discuss pricing simple credit derivatives. We will also discuss correlation products and the most common techniques used for their pricing. In the third part, we will discuss some recent research papers addressing the use of adjoint algorithmic differentiation for the calculation of risk for interest rate and credit derivatives

IEOR E4725 Topics in Quantitative Finance: Numerical Solutions of Partial Differential Equation. 3 points.

Lect: 3.Not offered during 2023-2024 academic year.

Prerequisites: (IEOR E4706) and (IEOR E4707) IEOR E4700; Additional Prerequisites will be announced depending on offering

Networks are ubiquitous in our modern society. Economic and social networks have been used extensively to model a variety of situations, in which individual decision-makers are affected by the choices of their peers in the network. This course will introduce the main mathematical models for the study of these networks. It will discuss game theoretical and dynamic optimization techniques, which can be used to analyze a wide variety of these networks, including their resilience to shocks, the diffusion of information leading to social contagion, and how the strategic behavior of agents shapes the performance of the network

IEOR E4726 TOPICS IN QUANTATIVE FINANCE. 3.00 points.

Prerequisites: IEOR E4700; Additional prerequisites will be announced depending on offering
Selected topics of interest in the area of quantitative finance. Offerings vary each year; some topics include energy derivatives, experimental finance, foreign exchange and related derivative instruments, inflation derivatives, hedge fund management, modeling equity derivatives in Java, mortgage-backed securities, numerical solutions of partial differential equations, quantitative portfolio management, risk management, trade and technology in financial markets

IEOR E4727 TOPICS IN QUANTATIVE FINANCE. 3.00 points.

Prerequisites: IEOR E4700; Additional prerequisites will be announced depending on offering
Selected topics of interest in the area of quantitative finance. Offerings vary each year; some topics include energy derivatives, experimental finance, foreign exchange and related derivative instruments, inflation derivatives, hedge fund management, modeling equity derivatives in Java, mortgage-backed securities, numerical solutions of partial differential equations, quantitative portfolio management, risk management, trade and technology in financial markets

IEOR E4728 TOPICS IN QUANTITATIVE FINANCE. 1.50 point.

Prerequisites: IEOR E4700; Additional prerequisites will be announced depending on offering
The search for better performance has led investors to explore Alternative Investments that are outside the traditional categories of exchange traded equities, Treasury Bonds, and other investment-grade fixed income products. The field of Alternative Investments covers a wide range of products such as convertible bonds, Preferred Shares, Hedge Funds, Venture Capital, and Cryptocurrencies. There is a growing need in the market for students with knowledge of these products and the practical and theoretical know how of valuing and risk managing these investments given that each product has it own nuances and anomalies. This course presents and studies some major Alternative Investment products and ways to evaluate and risk manage them

IEOR E4729 TOPICS IN QUANTATIVE FINANCE. 1.50 point.

NOTE: If you have previously completed IEOR 4733 Algorithmic Trading or IEOR 4729 Model Based Trading, please note that while this version of 4729 has many new elements, it materially overlaps with these two classes and is intended to replace both.

Prerequisites: IEOR E4700; Additional prerequisites will be announced depending on offering.
Selected topics of interest in the area of quantitative finance. Offerings vary each year; some topics include energy derivatives, experimental finance, foreign exchange and related derivative instruments, inflation derivatives, hedge fund management, modeling equity derivatives in Java, mortgage-backed securities, numerical solutions of partial differential equations, quantitative portfolio management, risk management, trade and technology in financial markets

IEOR E4731 CREDIT RISK/CREDIT DERIVATIVES. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4701) and (IEOR E4707) IEOR E4701 AND IEOR E4707
Introduction to quantitative modeling of credit risk, with a focus on the pricing of credit derivatives. Focus on the pricing of single-name credit derivatives (credit default swaps) and collateralized debt obligations (CDOs). Detail topics include default and credit risk, multiname default barrier models and multiname reduced form models

IEOR E4732 COMPUT METHODS IN FINANCE. 3.00 points.

Prerequisites: (IEOR E4700) IEOR E4700
MS IEOR students only. Application of various computational methods/techniques in quantitative/computational finance. Transform techniques: fast Fourier transform for data de-noising and pricing, finite difference methods for partial differential equations (PDE), partial integro-differential equations (PIDE), Monte-Carlo simulation techniques in finance, and calibration techniques, filtering and parameter estimation techniques. Computational platform will be C /Java/Python/Matlab/R

IEOR E4733 ALGORITHMIC TRADING. 3.00 points.

Prerequisites: IEOR E4700 IEOR E4700
Prerequisite(s): IEOR E4700. Large and amorphous collection of subjects ranging from the study of market microstructure, to the analysis of optimal trading strategies, to the development of computerized, high-frequency trading strategies. Analysis of these subjects, the scientific and practical issues they involve, and the extensive body of academic literature they have spawned. Attempt to understand and uncover the economic and financial mechanisms that drive and ultimately relate them

IEOR E4734 FOR EXCH/RELATD DERIVATVS INST. 1.50 point.

1.5.

Prerequisites: (IEOR E4700) IEOR E4700
Foreign exchange market and its related derivative instruments—the latter being forward contracts, futures, options, and exotic options. What is unusual about foreign exchange is that although it can rightfully claim to be the largest of all financial markets, it remains an area where very few have any meaningful experience. Virtually everyone has traded stocks, bonds, and mutual funds. Comparatively few individuals have ever traded foreign exchange. In part that is because foreign exchange is an interbank market. Ironically the foreign exchange markets may be the best place to trade derivatives and to invent new derivatives—given the massive two-way flow of trading that goes through bank dealing rooms virtually 24 hours a day. And most of that is transacted at razor-thin margins, at least comparatively speaking, a fact that makes the foreign exchange market an ideal platform for derivatives. The emphasis is on familiarizing the student with the nature of the foreign exchange market and those factors that make it special among financial markets, enabling the student to gain a deeper understanding of the related market for derivatives on foreign exchange

IEOR E4735 Structured and Hybrid Products. 3 points.

Lect: 3.

Prerequisites: (IEOR E4700) Refer to course syllabus.

Conceptual and practical understanding of structured and hybrid products from the standpoint of relevant risk factors, design goals and characteristics, pricing, hedging and risk management. Detailed analysis of the underlying cash-flows, embedded derivative instruments and various structural features of these transactions, both from the investor and issuer perspectives, and analysis of the impact of the prevailing market conditions and parameters on their pricing and risk characteristics. Numerical methods for valuing and managing risk of structured/hybrid products and their embedded derivatives and their application to equity, interest rates, commodities and currencies, inflation and credit-related products. Conceptual and mathematical principles underlying these techniques, and practical issues that arise in their implementations in the Microsoft Excel/VBA and other programming environments. Special contractual provisions often encountered in structured and hybrid transactions, and attempt to incorporate yield curves, volatility smile, and other features of the underlying processes into pricing and implementation framework for these products.

IEOR E4736 EVENT DRIVEN FINANCE. 3.00 points.

Lect: 3.

Prerequisites: (IEOR E4706 and IEOR E4707) IEOR E4706: Foundations of Financial Engineering, IEOR E4707: Financial Engineering: Continuous Time Models, and students familiar with simple Black-Scholes pricing.

The course takes a long deep look at the actual behavior of real stocks and options in the presence of commonplace, but singular events, such as earnings take-overs, hard-to-borrowness, expirations, etc. The course introduces concepts to propose trading schema (we organize tests via the very extensive and robust IVY options/stock database) and carry out tests efficiently and accurately. It exposes students to the striking differences between the model-based (static, thermodynamic/SDE model solutions) behavior predicted for stocks and options and their real (often quite different) behavior. They will become familiar with computational techniques for modeling and testing proposals for trading strategies. 

IEOR E4737 AI Applications in Finance. 3.00 points.

Data, models, visuals; various facets of AI, applications in finance; areas: fund, manager, security selection, asset allocation, risk management within asset management; fraud detection and prevention; climate finance and risk; data-driven real estate finance; cutting-edge techniques: machine learning, deep learning in computational, quantitative finance; concepts: explainability, interpretability, adversarial machine learning, resilience of AI systems; industry utilization

IEOR E4739 PROGRAMMING FOR FE 2. 3.00 points.

Lect: 2.5.

Prerequisites: (IEOR E4738) IEOR E4738; and instructor’s permission.
Developing effective software implementations in C programming language; modeling of portfolio optimization; modeling of price impact trading models; review of synchronization of programs using the file system; review of synchronization of programs using threads; review of synchronization of programs using sockets; implementation of high-performance simulations in finance

IEOR E4741 Programming for Financial Engineering. 3.00 points.

Covers C programming language, applications, and features for financial engineering, and quantitative finance applications. Note: restricted to IEOR MS FE students only

IEOR E4742 Deep Learning for OR and FE. 3.00 points.

Selected topics of interest in area of quantitative finance. Some topics include energy derivatives, experimental finance, foreign exchange and related derivative instruments, inflation derivatives, hedge fund management, modeling equity derivatives in Java, mortgage-backed securities, numerical solutions of partial differential equations, quantitative portfolio management, risk management, trade and technology in financial markets. Note: open to IEOR students only

IEOR E4743 Financial Correlations – Modeling, Trading, Risk Management ＆ AI. 3.00 points.

Introduction to math finance, knowledge of working as a "Quant" in investment banking, hedge funds, algo shop, HFT firm, Fed, Exchange, SEC, IMF, back office, mutual fund, as a trader in risk management, product development, model validation, compliance, reporting, academia. Open only to master's students in IEOR department

IEOR E4744 Modeling ＆ Market Making in Foreign Exchange. 1.50 point.

Introduction to topics in modeling and market making in foreign exchange, such as spots markets, forward markets, vanilla option markets, exotic derivative markets, and algorithmic index markets. Open only to master's students in IEOR department

IEOR E4745 Applied Financial Risk Management. 3.00 points.

Prerequisites: Probability and statistics, instruments of the financial markets, and asset pricing models

Prerequisites: see notes re: points probability and statistics, instruments of the financial markets, and asset pricing models.
Introduces risk management principles, practical implementation and applications, standard market, liquidity, and credit risk measurement techniques, and their drawbacks and limitations. Note: restricted to IEOR students only

IEOR E4798 Financial Engineering Practitioners Seminar Series. 0.00 points.

Prerequisites: First year MSFE Students
Degree requirement for all MSFE first-year students. Topics in Financial Engineering. Past seminar topics include Evolving Financial Intermediation, Measuring and Using Trading Algorithms Effectively, Path-Dependent Volatility, Artificial Intelligence and Data Science in modern financial decision making, Risk-Based Performance Attribution, and Financial Machine Learning. Meets select Monday evenings

IEOR E4799 MSFE Quantitative and Computational Bootcamp. 0.00 points.

Primer on quantitative and mathematical concepts. Required of all incoming MSFE students

IEOR E4899 Research Training. 0.00 points.

Research training course. Recommended in preparation for laboratory related research

IEOR E4900 MASTERS RESEARCH OR PROJECT. 1.00-3.00 points.

Prerequisites: approval by a faculty member who agrees to supervise the work.
Prerequisite(s): Approval by a faculty member who agrees to supervise the work. Independent work involving experiments, computer programming, analytical investigation, or engineering design

IEOR E4998 MANAG TECH INNOV ＆ ENTREPRENEURSHIP. 3.00 points.

Lect: 3.

A required course for undergraduate students majoring in OR:EMS. Focus on the management and consequences of technology-based innovation. Explores how new industries are created, how existing industries can be transformed by new technologies, the linkages between technological development and the creation of wealth and the management challenges of pursuing strategic innovation

IEOR E4999 FIELDWORK. 1.00-1.50 points.

Prerequisites: Obtained internship and approval from faculty adviser.
Only for IEOR graduate students who need relevant work experience as part of their program of study. Final reports required. May not be taken for pass/fail credit or audited

IEOR E6003 Prof Dev for PHDS:3rd Yr . 3.00-3.50 points.

IEOR E6004 Professional Development and Leadership for PhDs - Fourth Year and Beyond. 3.00 points.

IEOR E6602 NONLINEAR ＆ CONVEX PROGRAMMING. 3.00 points.

Lect: 3.

Prerequisites: Ph.D.-level linear programming
Convex sets and functions, convex duality and optimality conditions. Computational methods: steepest descent, Newton and quasi-Newton methods for unconstrained problems, active set, penalty set, interior point, augmented Lagrangian and sequential quadratic programming methods for constrained problems. Introduction to nondifferentiable optimization and bundle methods

IEOR E6604 Advanced Combinatorial Optimization. 3.00 points.

Combinatorial optimization is an exciting area in the field of optimization, with connections to areas of purely theoretical research such as graph theory, theoretical computer science, and combinatorics, and used in applications such as market design, logistics, and machine learning, among others

IEOR E6608 INTEGER PROGRAMMING. 3.00 points.

 .

Category: MIA, MPA
Not offered during 2023-2024 academic year.

IEOR E6613 Optimization, I. 4.5 points.

Prerequisites: Linear algebra

Theory and geometry of linear programming. The simplex method. Duality theory, sensitivity analysis, column generation and decomposition. Interior point methods. Introduction to nonlinear optimization: convexity, optimality conditions, steepest descent and Newton's method, active set and barrier methods.

IEOR E6614 OPTIMIZATION II. 4.50 points.

Lect: 3.

Prerequisites: Linear algebra
An introduction to combinatorial optimization, network flows and discrete algorithms. Shortest path problems, maximum flow problems. Matching problems, bipartite and cardinality nonbipartite. Introduction to discrete algorithms and complexity theory: NP-completeness and approximation algorithms

IEOR E6616 CONVEX OPTIMIZATION. 3.00 points.

IEOR E6617 Machine Learning and High-Dimensional Data Analysis in Operations Research. 3.00 points.

Discusses recent advances in fields of machine learning: kernel methods, neural networks (various generative adversarial net architectures), and reinforcement learning (with applications in robotics). Quasi Monte Carlo methods in the context of approximating RBF kernels via orthogonal transforms (instances of the structured technique). Will discuss techniques such as TD(0), TD(λ), LSTDQ, LSPI, DQN

IEOR E6704 QUEUING THEORY ＆ APPLICATIONS. 3.00 points.

IEOR E6706 QUEUEING NETWORKS. 3.00 points.

IEOR E6711 STOCHASTIC MODELING I. 4.50 points.

Prerequisites: (STAT GU4001) or STAT W4001; STAT GU4001 or equivalent.
Advanced treatment of stochastic modeling in the context of queueing, reliability, manufacturing, insurance risk, financial engineering and other engineering applications. Review of elements of probability theory; exponential distribution; renewal theory; Wald’s equation; Poisson processes. Introduction to both discrete and continuous-time Markov chains; introduction to Brownian motion

IEOR E6712 STOCHASTIC MODELING II. 4.50 points.

Prerequisites: (IEOR E6711) or IEOR E6711; IEOR E6711 or equivalent.
Continuation of IEOR E6711, covering further topics in stochastic modeling in the context of queueing, reliability, manufacturing, insurance risk, financial engineering, and other engineering applications. Topics from among generalized semi-Markov processes; processes with a non-discrete state space; point processes; stochastic comparisons; martingales; introduction to stochastic calculus

IEOR E8100 ADVANCED TOPICS IN IEOR. 3.00 points.

Prerequisites: Faculty adviser’s permission.
Selected topics in IEOR. Content varies from year to year. May be repeated for credit

IEOR E9101 RESEARCH. 1.00-6.00 points.

Before registering, the student must submit an outline of the proposed work for approval by the supervisor and the chair of the Department. Advanced study in a specialized field under the supervision of a member of the department staff. May be repeated for credit

IEOR E9800 DOCTORAL RESEARCH INSTRUCTION. 3.00-12.00 points.

MEIE E4810 INTRO TO HUMAN SPACE FLIGHT. 3.00 points.

Prerequisites: Department permission and knowledge of MATLAB or equivalent.
Introduction to human spaceflight from a systems engineering perspective. Historical and current space programs and spacecraft. Motivation, cost, and rationale for human space exploration. Overview of space environment needed to sustain human life and health, including physiological and psychological concerns in space habitat. Astronaut selection and training processes, spacewalking, robotics, mission operations, and future program directions. Systems integration for successful operation of a spacecraft. Highlights from current events and space research, Space Shuttle, Hubble Space Telescope, and International Space Station (ISS). Includes a design project to assist International Space Station astronauts

MSIE W6408 Inventory Theory. 3 points.

Lect: 3.Not offered during 2023-2024 academic year.

Prerequisites: probability theory, dynamic programming.

Construction and analysis of mathematical models used in the design and analysis of inventory systems. Deterministic and stochastic demands and lead times. Optimality of (s, S) policies. Multiproduct and multiechelon systems. Computational methods.

ORCS E4200 Data-driven Decision Modeling. 3.00 points.

Introduction to modeling, estimating, and solving decision-making problems in the context of artificial intelligence and analytics. Potential topics include choice models, quantity models, online learning using multi-armed bandits, dynamic decision modeling, dynamic games, and Bayesian learning theory. Practice both theory and applications using Python programming

ORCS E4201 Policy for Privacy Technologies. 3.00 points.

Introduction to privacy technologies, their use in practice, and privacy regulations. Potential topics include anonymization, differential privacy, cryptography, secure multi-party computation, and legislation. Course material will be abased in real-world use cases of these tools

ORCS E4529 Reinforcement Learning. 3.00 points.

Prerequisites: Probability and statistics, basic optimization e.g., familiarity with linear and convex optimization, gradient descent, basic algorithm design constructs, familiarity with programming in python.
Markov Decision Processes (MDP) and Reinforcement Learning (RL) problems. Reinforcement Learning algorithms including Q-learning, policy gradient methods, actor-critic method. Reinforcement learning while doing exploration-exploitation dilemma, multi-armed bandit problem. Monte Carlo Tree Search methods, Distributional, Multi-agent, and Causal Reinforcement Learning

ORCA E2500 FOUNDATIONS OF DATA SCIENCE. 3.00 points.

Prerequisites: MATH UN1101 and MATH UN1102 MATH V1101 AND MATH V1102; Some familiarity with programming.
Designed to provide an introduction to data science for sophomore SEAS majors. Combines three perspectives: inferential thinking, computational thinking, and real-world applications. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? Teaches critical concepts and skills in computer programming, statistical inference, and machine learning, in conjunction with hands-on analysis of real-world datasets such as economic data, document collections, geographical data, and social networks. At least one project will address a problem relevant to New York City

CSOR E4010 GRAPH THEORY: COMBINATL VIEW. 3.00 points.

Lect: 3.Not offered during 2023-2024 academic year.

Prerequisites: Linear algebra, or instructor’s permission.
An introductory course in graph theory with emphasis on its combinatorial aspects. Basic definitions, and some fundamental topics in graph theory and its applications. Topics include trees and forests graph coloring, connectivity, matching theory and others

ORCS E4200 Data-driven Decision Modeling. 3.00 points.

Introduction to modeling, estimating, and solving decision-making problems in the context of artificial intelligence and analytics. Potential topics include choice models, quantity models, online learning using multi-armed bandits, dynamic decision modeling, dynamic games, and Bayesian learning theory. Practice both theory and applications using Python programming

ORCA E4500 FOUNDATIONS OF DATA SCIENCE. 3.00 points.

Prerequisites: MATH V1101 AND MATH V1102; Some familiarity with programming
Designed to provide an introduction to data science for sophomore SEAS majors. Combines three perspectives: inferential thinking, computational thinking, and real-world applications. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? Teaches critical concepts and skills in computer programming, statistical inference, and machine learning, in conjunction with hands-on analysis of real-world datasets such as economic data, document collections, geographical data, and social networks

ORCS E4529 Reinforcement Learning. 3.00 points.

Prerequisites: Probability and statistics, basic optimization e.g., familiarity with linear and convex optimization, gradient descent, basic algorithm design constructs, familiarity with programming in python.
Markov Decision Processes (MDP) and Reinforcement Learning (RL) problems. Reinforcement Learning algorithms including Q-learning, policy gradient methods, actor-critic method. Reinforcement learning while doing exploration-exploitation dilemma, multi-armed bandit problem. Monte Carlo Tree Search methods, Distributional, Multi-agent, and Causal Reinforcement Learning

ORCS E4201 Policy for Privacy Technologies. 3.00 points.

Introduction to privacy technologies, their use in practice, and privacy regulations. Potential topics include anonymization, differential privacy, cryptography, secure multi-party computation, and legislation. Course material will be abased in real-world use cases of these tools