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Course Criteria
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4.00 Credits
. This course presents the mathematical foundations of AI, including probability, decision theory and machine learning.
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4.00 Credits
With the increasing diversity and complexity of computers and their applications, the development of efficient, reliable software has become increasingly dependent on automatic support from compilers & other program analysis and translation tools. This course covers principal topics in understanding and transforming programs at the assembly, function, and program levels. Specific techniques for imperative languages include data flow, dependence, and inter-procedural analyses; resource allocation; and program transformation for locality and parallelism. The course will also touch on theoretical issues in program semantics for higher order languages. Course projects include a program analyzer and optimizer for a subset of the C programming language. Meets jointly with CSC 455, a graduate-level course that requires additional readings and assignments.
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4.00 Credits
Principles of parallel and distributed systems, and the associated implementation and performance issues. Topics covered will include programming interfaces to parallel and distributed computing, interprocess communication, synchronization, and consistency models, fault tolerance and reliability, distributed process management, distributed file systems, multiprocessor architectures, parallel program optimization, and parallelizing compilers. Students taking this course at the 400 level will be required to complete additional readings and/or assignments.
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4.00 Credits
Advanced study of design and analysis of algorithms. Topics typically include: growth of functions; recurrences; probabilistic analysis and randomized algorithms; maximum flow; sorting networks; expander graphs; matrix operations; linear programming; discrete Fourier transform; number-theoretic algorithms; string matching; computational geometry; NP-completeness; approximation algorithms. Students taking this course at the 400 level may be required to complete additional tests, readings or assignments.
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0.00 Credits
No course description available.
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0.00 Credits
No course description available.
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4.00 Credits
Introduces many aspects of neuroengineering research, with an emphasis on biologically plausible models of neurons, circuits, and systems
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4.00 Credits
This course introduces students to the theory and practice of control systems engineering. Topics include frequency domain modeling, time domain stability, transient and steady-state error analysis, root locus and frequency response techniques and feedback system design. Emphasis is placed on analyzing physiological control systems, but the concepts and design techniques are applicable and applied to a wide variety of other systems including mechanical and electrical systems. Graduate students will have more homework problems and additional exam problems.
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4.00 Credits
No course description available.
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4.00 Credits
The course presents the physical basis for the use of high-frequency sound in medicine. Topics include acoustic properties of tissue, sound propagation (both linear and nonlinear) in tissues, interaction of ultrasound with gas bodies (acoustic cavitation and contrast agents), thermal and non-thermal biological effects of utrasound, ultrasonography, dosimetry, hyperthermia and lithotripsy. This course is the graduate complement to BME251.
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