Engineering

This course introduces the fundamental theory and concepts of computational intelligence methods, in particular neural networks, fuzzy systems, genetic algorithms and their applications in the area of machine intelligence. Topics include: (1) Understand the concepts of fuzzy sets, knowledge representation using fuzzy rules, approximate reasoning, fuzzy inference systems, and fuzzy logic control and other machine intelligence applications of fuzzy logic. (2) Understand the basics of an evolutionary computing paradigm known as genetic algorithms and its application to engineering optimization problems. (3) Understand the fundamental theory and concepts of neural networks, neuro-modeling, several neural network paradigms and its applications. (4) Contents: Introduction to Fuzzy Logic. Introduction to Fuzzy Sets. Introduction to Fuzzy Inference Systems. Fuzzy Logic Applications. Introduction to Genetic Algorithm. Fundamental Concepts of Artificial Neural Networks and Neural Network Architectures.

This course introduces the essential software engineering body of knowledge, including software project management, software requirements and specifications, software design, and software testing and maintenance.

This course addresses the design and performance tuning of database applications, focusing on relational database applications implemented with relational database management systems. Topics covered include normalization theory (functional, multi-valued and join dependency, normal forms, decomposition and synthesis methods), entity relationship approach and SQL tuning (performance evaluation, execution plan verification, indexing, de-normalization, code level and transactions tuning). Additional selected topics include the technologies, design and performance tuning of non-relational database applications (for instance, network and hierarchical models and nested relational model for an historical perspective, as well as XML and NoSQL systems for a modern perspective). The course requires students to take prerequisites.

This course covers healthcare delivery systems, healthcare technology-human integration, human factors in healthcare, crew resource management, quality of care, economic analysis in healthcare, healthcare logistics, healthcare system test and evaluation, and analysis and design for patient safety.

This course introduces the basic concepts and techniques of planning, design and operations within a facility. Topics include forecasting techniques, aggregate planning, inventory management, material requirements planning, process planning, production systems and operations scheduling. Students examine the intuitions behind many manufacturing logistics concepts and demonstrate the application of operations research techniques to this area. The course requires students to take prerequisites.

This course covers mathematical concepts and algorithms that allow society to recover the 3D geometry of camera motions and the structures in its environment. Topics include projective geometry, camera model, one-/two-/three-/N-View reconstructions and stereo, generalized cameras and non- rigid structure-from-motion. The course requires students to take prerequisites.

This course furthers the fundamental mathematical knowledge and skills that are necessary in engineering. Topics include complex numbers, vectors, matrices, limits and continuity of functions, derivatives and integration and their applications, multivariable calculus, partial derivatives, ordinary differential equations, double integrals in polar coordinates, dot product, and cross product. The course requires students to take prerequisites.

A robot is an intelligent mechanical system with multiple degrees of freedom. This course investigates the fundamentals of modeling and control of a robot manipulator. The course covers spatial descriptions and transformations; manipulator kinematics, and manipulator dynamics. 

Required Course Prerequisites: Linear Algebra and Control Engineering I. 

This course introduces methods for creating systems that use data intelligently to improve themselves. This requires combining human intelligence (using methods like crowdsourcing, collaborative design) with artificial intelligence (discovering which technology designs help which people) through designing randomized A/B experiments that are collaborative, dynamic, and personalized. The course requires students to take prerequisites.