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Course Criteria
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3.00 Credits
Methodologies will be formulated for the design, implementation, testing and documentation of programming projects, with a focus on solving informatics problems in the biological and medical sciences. Functional decomposition will be introduced for problems using a proprietary language (Mathematica) and object oriented solutions will also be introduced and implemented for problems using an open source language commonly used in the field of bioinformatics and medicine (Python). C++ object oriented concepts are introduced where appropriate. Prerequisites: BIOL 118, BE 202 or equivalent programming experience. fall (occasional), 3 cr.
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3.00 Credits
Introduces students to basic concepts of experimental design and data acquisition from living systems. Extends student?s data analysis capabilities from simple statistical description to statistical inference; introduces multivariate analytical techniques. Prerequisites: BE 202, ISE 261. fall, 4 cr.
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3.00 Credits
Focuses upon the physical biology of flow, a topic of importance for a wide range of disciplines including bioengineering, biology, zoology, environmental studies, chemistry, physics and other areas of engineering. The physical phenomena of fluid mechanics as they relate to living systems are presented with an interest in examining the relationship between fluids and the functioning organisms, and between fluid flow and biological design. occasional, 3 cr.
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3.00 Credits
Extends concepts of emergent behavior previously studied in the discrete-time/space domains into the continuous-time/space domains using ordinary and partial differential equations. Linear and nonlinear dynamical systems theories are discussed in depth and applied to modeling and analysis of complex biological systems. Emphasizes the broad applicability of complex systems perspective to a wide range of situations. Prerequisite: MATH 371 or equivalent. spring, 3 cr.
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3.00 Credits
Develops and implements new medical diagnostic algorithms using concepts derived from statistical learning and computational intelligence theory. Emphasis is placed on intelligent diagnosis derived from analog and digital medical, tissue and microarray images, using second opinion Computer Aided Diagnostic (CAD) software. Prerequisites: ISE 261 or MATH 327, BE 203. occasional, 3 cr.
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1.00 Credits
431 PROFESSIONAL SKILLS V, VI Continues development of the non-technical skills essential to effective engineering, with specific emphasis on career development, including interviewing, negotiating, personal finance and ethics. fall/spring, 1 cr. each
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3.00 Credits
This first semester of the capstone design experience focuses on integrating the content of the professional skills sequence (directed toward promoting entrepreneurial engineering) and the didactic core sequence (directed toward developing an understanding of biological and biomimetic systems as complex systems). Emphasis is on the process of innovation, specifically, team development of a ?proof of principle? of a technology that can subsequently be ?evolved? into a commercially realizable product. Prerequisite: senior standing. fall, 4 cr.
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3.00 Credits
Continuation of the capstone design experience. Emphasis is on learning to move a nascent product concept across the fitness landscape (application, form and user dimensions) and developing a plan to ensure successful market introduction of the concept. Prerequisite: BE 450. spring, 4 cr.
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1.00 Credits
Investigation of the behavior of social agents in a laboratory setting where students play the role of the agents. Wireless computer mediated communication system permits one-one, one-many and many-many interactions in one-time and repeated situations. Laboratory permits quantification of reasoned responses among rational and irrational players. Introduction to the theory of strategic games. Prerequisite: none. spring, 3 cr.
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3.00 Credits
Introduces the neurobiology of learning and reinforcement learning. Extends student?s understanding of neural networks to include recurrent networks and unsupervised learning. Underlying theme is the utilization of robotic devices that permit students to study sensory feedback and compare the advantages and disadvantages of specific learning algorithms (habituation, sensitization, reinforcement learning) in the physical real world. Prerequisites: BE 302, MATH 371. fall, 4 cr.
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