MBA and Master's in Electrical Engineering Dual Degree Program

This course introduces managers to the essence of business economics – the theories, concepts and ideas that form the economist’s tool kit encompassing both the microeconomic and macroeconomic environments. Microeconomic topics include demand and supply, elasticity, consumer choice, production, cost, profit maximization, market structure, and game theory while the Macroeconomic topics will be GDP, inflation, unemployment, aggregate demand, aggregate supply, fiscal and monetary policies. In addition the basic concepts in international trade and finance will be discussed.

This course will provide the student with the principles and techniques of financial and managerial accounting for technical organizations. The emphasis will be on the use of financial data for decision making. The basics of accounting will briefly be covered, with the major amount of time spent on ways to understand, analyze and use the data for decision making. Budgeting and analysis of performance, as well as, recognizing fixed and variable expenses are other key areas of financial management. Issues of valuation, time value of money, uncertainty and risk will be integrated in the material. The one-term course will make extensive use of the text by Weygandt et al (see below), supplemented by cases, exercises, homework and examinations. Emphasis will be on real-world, practical application of the tools of finance to management decision making.

This course provides students with a rigorous introduction to the fundamental principles of finance. The primary concepts covered include the time value of money, principles of valuation and risk. Specific applications include the valuation of debt and equity securities as well as capital budgeting analysis, financial manager’s functions, liquidity vs profitability, financial planning, capital budgeting, management of long term funds, money and capital markets, debt and equity, management of assets, cash and accounts receivable, inventory and fixed assets. Additional topics include derivative markets.

Project success depends, largely, on the human side. Success in motivating project workers, organizing and leading project teams, communication and sharing information, and conflict resolution, are just a few areas that are critical for project success. However, being primarily technical people, many project managers tend to neglect these "soft" issues, assuming they are less important or that they should be addressed by direct functional managers. The purpose of this course is to increase awareness of project managers to the critical issues of managing people and to present some of the theories and practices of leading project workers and teams.

Executives make decisions every day in the face of uncertainty. The objective of this course is to help students understand how decisions are made, why they are often less than optimal, and how decision-making can be improved. This course will contrast how managers do make decisions with how they should make decisions, by thinking about how “rational” decision makers should act, by conducting in-class exercises and examining empirical evidence of how individuals do act (often erroneously) in managerial situations. The course will include statistical tools for decision-making, as well as treatment of the psychological factors involved in making decisions.

The study of marketing principles from the conceptual, analytical, and managerial points of view. Topics include: strategic planning, market segmentation, product life-cycle, new product development, advertising and selling, pricing, distribution, governmental, and other environmental influences as these factors relate to markets and the business structure. 

Project success depends, largely, on the human side. Success in motivating project workers, organizing and leading project teams, communication and sharing information, and conflict resolution, are just a few areas that are critical for project success. However, being primarily technical people, many project managers tend to neglect these "soft" issues, assuming they are less important or that they should be addressed by direct functional managers. The purpose of this course is to increase awareness of project managers to the critical issues of managing people and to present some of the theories and practices of leading project workers and teams.

An interdisciplinary course which examines the elements of, and the framework for, developing and implementing organizational strategy and policy in competitive environments. The course analyzes management problems both from a technical-economic perspective and from a behavioral perspective. Topics treated include: assessment of organizational strengths and weaknesses, threats, and opportunities; sources of competitive advantage; organizational structure and strategic planning; and leadership, organizational development, and total quality management. The case method of instruction is used extensively in this course.

Field Consulting Project Capstone Course

The theory of linear algebra with application to state space analysis. Topics include Cauchy-Binet and Laplace determinant theorems, system of linear equations; linear transformations, basis and rank; Gaussian elimination; LU and congruent transformations; Gramm-Schmidt; eigenvalues, eigenvectors and similarity transformations; canonical forms; functions of matrices; singular value decomposition; generalized inverses; norm of a matrix; polynomial matrices; matrix differential equations; state space; controllability and observability.

Axioms of probability; discrete and continuous random vectors; functions of random variables; expectations, moments, characteristic functions, and moment generating functions; inequalities, convergence concepts, and limit theorems; central limit theorem; and characterization of simple stochastic processes: widesense stationality and ergodicity.

This course presents tool, techniques, algorithms, and programming techniques using the Python programming language for data intensive applications and decision making. The course formally introduces techniques to: (i) gather,(ii) store, and (iii) process large volumes of data to make informed decisions. Such techniques find applicability in many engineering application areas, including communications systems, embedded systems, smart grids, robotics, Internet, and enterprise networks, or any network where information flows and alters decision making.

An introduction to classic and modern feedback control that does not presume an undergraduate background in control. Transfer function and state space modeling of linear dynamic systems, closed-loop response, root locus, proportional, integral, and derivative control, compensators, controllability, observability, pole placement, linear–quadratic cost controllers, and Lyapunov stability. MATLAB simulations in control system design.

Fourier transforms; distribution theory; Gibbs phenomena; Shannon sampling; Poisson sums; discrete and fast Fourier transforms; Laplace transforms; z-transforms; the uncertainty principle; Hilbert transforms; computation of inverse transforms by contour integration; stability and realization theory of linear, time invariant, continuous and discrete systems.

This course will deal with the main aspects of applied modeling and optimization suitable for engineering, science, and business students. Sample applications to be used as case studies include channel capacity computation (information theory), statistical detection and estimation (signal processing), sequential decision making/revenue maximization (business), and others. Topics will include introduction to convex and non-linear optimization and modeling; linear, quadratic, and geometric program models and applications; stochastic modeling; combinatorial issues; gradient techniques; machine learning algorithms; stochastic approximation; genetic algorithms; and ant colony optimization.

Review of probability theory with applications to digital communications, digital modulation techniques, receiver design, bit error rate calculations, bandwidth efficiency calculations, convolutional encoding, bandwidth efficient coded modulation, wireless fading channel models, and shannon capacity, software simulation of communication systems.

This course explores data-driven methods that are used to analyze and solve complex business problems. Students will acquire analytical skills in building, applying and evaluating various models with hands-on computer applications. Topics include descriptive statistics, time-series analysis, regression models, decision analysis, Monte Carlo simulation and optimization models.

Artificial Intelligence (AI) is an interdisciplinary field that draws on insights from computer science, engineering, mathematics, statistics, linguistics, psychology, and neuroscience to design agents that can perceive the environment and act upon it. This course surveys applications of artificial intelligence to business and technology in the digital era, including autonomous transportation, fraud detection, machine translation, meeting scheduling, and face recognition. In each application area, the course focuses on issues related to management of AI projects, including fairness, accountability, transparency, ethics, and the law.

This course covers the general area of management of operations, both manufacturing and non-manufacturing. The focus of the course is on productivity and total quality management. Topics include quality control and quality management, systems of inventory control, work and materials scheduling, and process management.