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Course Criteria
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3.00 Credits
spring. Electronics magneto-statics, circuit, theory, Maxwell's equations, electromagnetic field theory, dielectrics, displacement current, current density, Poynting vector, scalar, and vector potentials. Prerequisites: MATH 286, PHYS 230/231, and PHYS 385.
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3.00 Credits
spring, odd years. Light, lenses, aberrations, diffraction, interference, polarization, resolving power, coherence, Fourier transforms, spectroscopy. Concurrent enrollment in PHYS 363 is required. Prerequisites: PHYS 122/123; MATH 186.
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1.00 Credits
spring, odd years. Concurrent enrollment in PHYS 362.
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3.00 Credits
fall. Vector methods of static systems of forces, couples and moments, particle dynamics, rigid body dynamics, work, kinetic and potential energy, momentum and the conservation laws, and Hamilton and Lagrangian methods. Prerequisites: PHYS 230/231; MATH 361.
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3.00 Credits
fall, odd years. A study of Newtonian physics, evolution, thermodynamics, quantum theory, relativity, and the impact of these sciences on religion, philosophy, literature, the arts, and the social sciences. Same as PHIL 378. May be used to satisfy Science University Core requirements. For non-science majors.
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3.00 Credits
spring, odd years. A study of the relations of God, humans, and nature from the perspectives of philosophy, religion and science. Emphasis on the mutual impacts of science and religion. Same as PHIL 379. May be used to satisfy Science University Core requirements. For non-science majors.
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3.00 Credits
fall. Partial differential equations and boundary value programs, orthogonal functions, complex variables, Green's functions, and matrix theory. Prerequisite: MATH 361.
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3.00 Credits
fall, odd years. Active and passive electrical networks, time and frequency domains, Fourier and Laplace transform techniques, and some non-linear circuits. Prerequisite: MATH 361. Same as ENGR 388.
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3.00 Credits
spring, odd years. Advanced techniques of computing in physics, including error analysis, data fitting and analysis, adaptive quadrature and Monte Carlo methods, ordinary and partial differential equations, and finite element techniques, all with an introduction to parallel programming techniques. Prerequisites: MATH 341 and 377 and at least one of PHYS 360, 371, or 491.
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3.00 Credits
spring, even years. Introduces pattern recognition methods and applications: decision theory, Principal Component Analysis, neural networks, linear and non-linear discriminant functions, supervised and unsupervised learning, feature extraction, Hidden Markov Models, Kalman Filters, and related methods in the area of applied computing. Prerequisites: MATH 341 and 377 and at least one of PHYS 360, 371, or 491.
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