MBA and Master's in Applied Artificial Intelligence 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.

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.

In this course, students will evaluate and create their own prospective business strategies. They will develop an understanding of entrepreneurship and innovation in starting and growing a business venture. Students will be given an opportunity to actually start their own business or create a business in their company by learning how to take advantage of the new order of business opportunities of the information age. This course’s main objective is to show students how to identify these opportunities, be able to formulate and evaluate both qualitatively and quantitatively whether the opportunity is worth pursuing, and, of course, how it may be pursued. Actual case studies and experiences will be intertwined with the course content.

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

This course covers 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.

• 1 mathematical foundation course from the Master of Engineering degree program (3 credits)

• 4 core courses from the Master of Engineering degree program (12 credits)

• 3 concentration courses in Business Operations for ECE (9 credits)

• Project or Thesis Track

  • Project Track - 3 credits project course plus 3 credit electives + 0 credit research seminar course (6 Credits)

  • Thesis Track - 6 credit thesis research (6 credits)

Project Track

3 credit project course (800 course) and a 3 credit elective course at the 500 or 600 level. Students who enroll in the 3 credit project course (800 course) are required to enroll in the 0-credit co-requisite research seminar course, EE 820. The 3 credit elective course can be any graduate level course at the 500 or 600 levels within the Department of Electrical and Computer Engineering. Elective courses that are taken outside of the department require approval by the faculty advisor.

Thesis Track

6 credit thesis course (900 course). Students need to take the first 3 credit thesis course (900 course) in the third semester and the second 3 credit thesis course (900 course) in the fourth semester.

• 1 mathematical foundation course from the Master of Engineering degree program (3 credits)

• 4 core courses from the Master of Engineering degree program (12 credits)

• 3 concentration courses in Business Operations for ECE (9 credits)

• 2 elective courses from Master of Engineering degree program (6 credits)

Students in the Master of Engineering program are required to complete two elective courses (6 credits). Elective courses can be any graduate level course at the 500 or 600 levels within the Department of Electrical and Computer Engineering. Elective courses that are taken outside of the department require approval by the faculty advisor.

An introduction course for machine learning theory, algorithms and applications. This course aims to provide students with the knowledge in understanding key elements of how to design algorithms/systems that automatically learn, improve and accumulate knowledge with experience. Topics covered in this course include decision tree learning, neural networks, Bayesian learning, reinforcement learning, ensembling multiple learning algorithms, and various application problems. The students will have chances to simulate their algorithms in a programming language and apply them to solve real-world problems.

This course is designed to enhance ECE’s students knowledge in core subjects with the ability of analyzing big data applications. It will cover both the computational techniques, and the mathematical intuitions in the skill sets for the big data analytics. This class will provide students with the necessary data engineering processing skills, refined data optimizations for feature engineering, and sophisticated linear analysis for data transform and model ensembling.

This course will provide a comprehensive introduction on deep learning techniques used by practitioners in industry, with a focus on programming exercises using deep learning software packages. The course starts with a brief overview on statistics, linear algebra, and machine learning basics, and emphasizes teaching the analytical tools and the programming skills for applying deep neural networks for different application scenarios. By the end of the course, students will have a thorough knowledge on the state-of-the-art approaches used in deep learning for engineering applications.

Introduction and general pattern recognition concerns and statistical pattern recognition: introduction to statistical pattern recognition, supervised learning (training) using parametric and nonparametric approaches, parametric estimation and supervised learning, maximum likelihood (ML) estimation, the Bayesian parameter estimation approach, supervised learning using nonparametric approaches, Parzen windows, nonparametric estimation, unsupervised learning and clustering, and formulation of unsupervised learning problems; syntactic pattern recognition: quantifying structure in pattern description and recognition, grammar-based approach and applications, elements of formal grammars, syntactic recognition via parsing and other grammars, graphical approaches, and learning via grammatical inference; neural pattern recognition: the artificial neural network model, introduction to neural pattern associators and matrix approaches, multilayer, feed-forward network structure, and content addressable memory approaches. The Hopfield approach to pattern recognition, unsupervised learning, and self-organizing networks.

Part I: Introduction to game theory and evolutionary algorithms: games in strategic form and Nash equilibrium, existence and properties of Nash equilibrium, Pareto efficiency, extensive form games, repeated games, Bayesian games and Bayesian equilibrium, types of games and equilibrium properties, learning in games, evolutionary algorithms. Part II: Engineering applications of game theory and evolutionary algorithms. Examples may include: network optimization, cognitive radio networks, internet of things, smart health, smart grids, security applications.

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.

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 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.