MBA x Artificial Intelligence Concentration

Introduces systematic problem solving through programming. Offers students an opportunity to learn how to analyze a problem, how to divide and organize the problem into appropriate components, how to describe the problem in a computer language, how to analyze and understand the behavior of their programs, and how to test that their programs are working correctly. Additionally, introduces a method of program design called object-oriented programming and various ways to organize data, including a discussion of their advantages and disadvantages. To practice the course concepts, students undertake assignments ranging from small, highly specified programming tasks to larger open-ended problems where students design and code their own solutions.

CS 5001 | 4 Hours

Introduces the mathematical structures and methods that form the foundation of computer science. Studies structures such as sets, tuples, sequences, lists, trees, and graphs. Discusses functions, relations, ordering, and equivalence relations. Examines inductive and recursive definitions of structures and functions. Covers principles of proof such as truth tables, inductive proof, and basic logic and the counting techniques and arguments needed to estimate the size of sets, the growth of functions, and the space-time complexity of algorithms. Also, discusses data structures such as arrays, stacks, queues, lists, and the algorithms that manipulate them.

CS 5002 | 4 Hours

Presents a comparative approach to object-oriented programming and design. Discusses the concepts of object, class, metaclass, message, method, inheritance, and genericity. Reviews forms of polymorphism in object-oriented languages. Contrasts the use of inheritance and composition as dual techniques for software reuse—forwarding vs. delegation and subclassing vs. subtyping. Offers students an opportunity to obtain a deeper understanding of the principles of object-oriented programming and design, including software components, object-oriented design patterns, and the use of graphical design notations such as UML (unified modeling language). Illustrates basic concepts in object-oriented design with case studies in application frameworks and by writing programs in Java.

CS 5004 | 4 Hours

Introduces the basic principles and techniques for the design and implementation of efficient algorithms and data representations. Considers divide-and-conquer algorithms, graph traversal algorithms, linear programming, and optimization techniques. Covers the fundamental structures for representing data, such as hash tables, trees, and graphs.

CS 5006 | 2 Hours

Introduces the basic design of computing systems, computer operating systems, and assembly language using a RISC architecture. Describes caches and virtual memory. Covers the interface between assembly language and high-level languages, including call frames and pointers; the use of system calls and systems programming to show the interaction with the operating system; and the basic structures of an operating system, including application interfaces, processes, threads, synchronization, interprocess communication, deadlock, memory management, file systems, and input/output control.

CS 5007 | 2 Hours

Introduces the fundamental problems, theories, and algorithms of the artificial intelligence field. Topics include heuristic search and game trees, knowledge representation using predicate calculus, automated deduction and its applications, problem solving and planning, and introduction to machine learning. Required course work includes the creation of working programs that solve problems, reason logically, and/or improve their own performance using techniques presented in the course. Requires experience in Java programming.

CS 5100 | 4 Hours

Offers an overview of the wide range of AI techniques that exploit knowledge of the domain and humans to facilitate interaction between humans and systems, mediate human-human interaction, leverage humans to improve system performance, and promote beneficial outcomes at the social and individual level. Topics can include AI/human computation, plan and activity recognition, smart sensing/homes, active learning, preference elicitation, intelligent/adaptive user interfaces, and mixed human-agent simulations. Studies how to design and develop intelligent interaction technologies while also critically assessing their social and ethical impact.

CS 5170 | 4 Hours

Provides a broad look at a variety of techniques used in machine learning and data mining, and also examines issues associated with their use. Topics include algorithms for supervised learning including decision tree induction, artificial neural networks, instance-based learning, probabilistic methods, and support vector machines; unsupervised learning; and reinforcement learning. Also covers computational learning theory and other methods for analyzing and measuring the performanceof learning algorithms. Course work includes a programming term project.

CS 6140 | 4 Hours