Graduate Courses
Graduate Courses
ITIS 6530 Systems Dynamics
Cross-listed as ITIS 8530 and DSBA 6530
Prerequisite: Permission of instructor.
Introduction to systems thinking and the systems dynamics worldview, tools for eliciting and mapping the structure and dynamics of complex systems, tools for modeling and simulation of complex systems, and procedures for testing and improving models. Helps students outline and evaluate dynamic relationships and factors that influence organizations’ performance, market position, decision-making, and policy evaluations. Integrates concepts across information systems, computer science, business, engineering, economics, and social sciences. Based on 3-hour weekly lectures and hands-on project assignment.
ITIS 6520 Network Science
Cross-listed as ITIS 8520 and DSBA 6520
Prerequisite: Permission of instructor.
Network Science helps students design faster, more resilient communication networks; revise infrastructure systems such as electrical power grids, telecommunications networks, and airline routes; model market dynamics; understand synchronization in biological systems, and analyze social interactions among people. It examines the various kinds of networks (regular, random, small-world, influence, scale-free, and social) and applies network processes and behaviors to emergence, epidemics, synchrony, and risk. This course integrates concepts across computer science, biology, physics, social network analysis, economics, and marketing.
ITCS 6500 Complex Adaptive Systems
Cross-listed as ITCS 8500, ITIS 6500/8500, and DSBA 6500
Prerequisite: Permission of instructor.
Complex adaptive systems (CAS) are networked (agents/part interact with their neighbors and, occasionally, distant agents), nonlinear (the whole is greater than the sum of its parts), adaptive (the system learns to change with its environment), open (new resources are being introduced into the environment), dynamic (the change is a norm), emergent (new, unplanned features of the system get introduced through the interaction of its parts/agents), and self-organizing (the parts organize themselves into a hierarchy of subsystems of various complexity). Ant colonies, networks of neurons, the immune system, the Internet, social institutions, organization of cities, and the global economy are a few examples where the behavior of the whole is much more complex than the behavior of the parts. This course will cover those and similar topics in an interactive manner. Examples of our current research effort will be provided. Topics include: Self-organization; emergent properties; learning; agents; localization affect; adaptive systems; nonlinear behavior; chaos; complexity. (On demand)
ITIS 6010 Advanced Complex Adaptive Systems
Cross-listed as ITIS 8010.
Prerequisite: 9 hours of graduate credit in computer science, or software and information systems.
Self-organization is a process where the organization of a system spontaneously increases. New, emergent properties appear. Complex adaptive systems like ant colonies, networks of neurons, the immune system, the Internet, and the global economy are a few examples where the behavior of the whole is much more complex than the behavior of the parts. This course will explore in greater detail some elements of self-organization and emergence. (On demand)
ITCS 6156 Machine Learning
Cross-listed as DSBA 6156.
Prerequisite: ITCS 6150 or permission of department.
Machine learning methods and techniques including acquisition of declarative knowledge; organization of knowledge into new, more effective representations; development of new skills through instruction and practice; and discovery of new facts and theories through observation and experimentation.
ITCS 6150 Intelligent Systems
Prerequisite: Full graduate standing or permission of department
To introduce core ideas in AI. Heuristic versus algorithmic methods; problem-solving; game playing and decision-making; automatic theorem proving; pattern recognition; adaptive learning; projects to illustrate theoretical concepts. (Fall) (Evenings)
ITCS 6111 Evolutionary Computation
Prerequisite: ITCS 6114 or permission of department
General introduction to optimization problems. Optimization techniques: hill climbing, simulated annealing, evolution strategies, and genetic algorithms. Evolution programming techniques. (Even years, Spring) (Evenings)
ITCS 6153 Neural Networks
Prerequisites: ITCS 6114.
Topics include basic notions and models of artificial neural nets; single layer neural classifiers; multilayer one-way neural nets; single layer feedback networks; neural models of associative memory; self-organizing neural nets; translation between neural networks and knowledge bases; applications of neural networks. (On demand)
ITCS 6170 Logic for Artificial Intelligence
Prerequisite: ITCS 6150 or permission of department.
Introduction to basic concepts of logic for artificial intelligence, including declarative knowledge, inference, resolution, non-monotonic reasoning, induction, reasoning with uncertain beliefs, distributed information systems, intelligent information systems, planning and intelligent agent architecture. (On demand)
ITCS 6050 Topics in Intelligent systems: computational human behavior modeling
Cross-listed as Topics in Psychology: Computational Human Behavior Modeling – PSYC 6099
Prerequisite: Permission of instructor.
The course covers inter-disciplinary topics relevant to creating models of human behavior from data – big and small.