Digi. Pen Course Descriptions MAT 1. Precalculus with Linear Algebra and Geometry (4 cr.)Prerequisite(s): None. This course presents fundamentals of college algebra and trigonometry, with an introduction to concepts in 2. D geometry and linear algebra. MAT 1. 05. Introductory Probability and Statistics (3 cr.)Prerequisite(s): None. This course presents fundamentals of probability and statistics without calculus.

MAT 1. 20. Mathematics of Music and Sound (3 cr.)Prerequisite(s): None. This course explores the mathematical foundations of music and sound. Topics include scale systems, just and tempered intervals, oscillations and trigonometry, sound waves, and basic discrete mathematics. MAT 1. 40. Linear Algebra and Geometry (4 cr.)Prerequisite(s): None. Credit may be received for either MAT 1.

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MAT 1. 40. but not for both. The two main themes throughout the course are vector.

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Topics from vector. Linear. transformations covered include rotations, reflections, shears. Students study the matrix representations. The main topics include limits, differentiation, and integration.

This course introduces the principles of animation through a variety of animation techniques. Topics include motion research and analysis, effective timing, spacing. More than twelve years have elapsed since the first public release of WEKA. X Family Letters Magazine Download Website here. In that time, the software has been rewritten entirely from scratch, evolved substantially. Building smart, inclusive, and resilient cities will help us to address some of the most challenging issues of our time. But how do city leaders from around the world. A3: Accurate, Adaptable, and Accessible Error Metrics for Predictive Models: abbyyR: Access to Abbyy Optical Character Recognition (OCR) API: abc: Tools for.

Limits include the graphical and intuitive computation of limits, algebraic properties of limits, and. Differentiation topics include techniques of differentiation, optimization, and applications to graphing. Integration includes Riemann sums, the definite integral, anti- derivatives, and the Fundamental Theorem of Calculus. MAT 1. 80. Vector Calculus I (4 cr.)Prerequisite(s): MAT 1.

Credit maybe received for either MAT 1. MAT 1. 80, but not for both. This course extends the standard calculus of one- variable. Vector. calculus is used in many branches of physics, engineering. Topics covered include limits, continuity, and. Laplacian, and applications.

Topics in integration include applications of the integral in physics and geometry and techniques of integration. The course also covers sequences and series of real numbers, power series and Taylor series, and calculus of transcendental. Further topics may include a basic introduction to concepts in multivariable and vector calculus.

MAT 2. 20. Mathematics of Digital Sound Processing (3 cr.)Prerequisite(s): MAT 2. MAT 2. 30. Credit may be received for MAT 2.

MAT 3. 20 but not for both. This course explores further topics in the mathematical. Topics include: Digital signals and sampling. FFT, convolution, filtering, wave equation, Bessel functions. The study of curves in two- and three space. TNB- frame. Additionally, the course may cover the basics of. MAT 2. 30. Vector Calculus II (4 cr.)Prerequisite(s): MAT 1.

Credit may be received for MAT 2. MAT 2. 30, but not for both. This course is a continuation of MAT 1. Topics covered include differential operators on vector fields, multiple integrals, line integrals, general change of variable formulas, Jacobi matrix, surface integrals, and various applications. The course also covers the theorems of Green, Gauss, and Stokes.

The more substantial part. Further topics may. MAT 2. 56. Introduction to Differential Equations (3 cr.)Prerequisite(s): MAT 2.

MAT 2. 30. This course introduces the basic theory and applications of first and second- order linear differential equations. The course emphasizes specific techniques such as the solutions to exact and separable equations, power series solutions, special functions and the Laplace transform. Applications include RLC circuits and elementary dynamical systems, and the physics of. MAT 2. 58. Discrete Mathematics (3 cr.)Prerequisite(s): MAT 2. MAT 2. 30. This course gives an introduction to several mathematical.

Typically starting with propositional and. Further topics include. Other topics may include graph theory.

MAT 3. 00. Curves and Surfaces (3 cr.)Prerequisite(s): MAT 2. MAT 2. 58. This course is an introduction to parameterized polynomial. It discusses both the algebraic and. Algebraic aspects include. Other topics may include an introduction.

MAT 3. 20. Mathematics of Digital Signal Processing I (3 cr.)Prerequisite(s): MAT 2. Credit may be received for MAT 3. MAT 2. 20 but not for both. Topics include: digital signals, sampling and quantization, complex numbers and phasors, complex functions, feedforward filters, feedback filters, frequency response and transfer functions, periodic signals and Fourier series, discrete Fourier transform and fast Fourier transform, comb and string filters, Z- transform and convolution. MAT 3. 21. Mathematics of Digital Signal Processing II (3 cr.)Prerequisite(s): MAT 3.

This course continues to explore the mathematical foundations of digital signal processing, with applications to digital audio programming. Topics include: Review of digital signals, Z- transforms and convolution, filter types, applications of fast Fourier transform, switching signals on and off, windowing, spectrograms, aliasing, digital to analog conversion, Nyquist Theorem, filter design, Butterworth filters, reverb, and the phase vocoder. MAT 3. 40. Probability and Statistics (3 cr.)Prerequisite(s): MAT 2. MAT 2. 30, MAT 2. This course is an introduction to basic probability and statistics with an eye toward computer science and artificial intelligence. Basic topics from probability theory include sample spaces, random variables, continuous and discrete probability density functions, mean and variance, expectation, and conditional probability.

Basic topics from statistics include binomial, Poisson, chi- square, and normal distributions; confidence intervals; and the Central Limit Theorem. Further topics may include fuzzy sets and fuzzy logic. MAT 3. 45. Introduction to Data Science (3 cr.)Prerequisite(s): MAT 1.

MAT 2. 58. This course presents a variety of computational tools for modeling and understanding complex data. Topics include manipulating data, exploratory data analysis, statistical inference, spam filters and na. The course will focus on both understanding the mathematics underlying the computational methods and gaining hands- on experience in the application of these techniques to real datasets. MAT 3. 50. Advanced Curves and Surfaces (3 cr.)Prerequisite(s): MAT 3. This course is a continuation of MAT 3. The course. treats some of the material from MAT 3.

B- spline. (NURBS) curves and surfaces, knot insertion, and subdivision. MAT 3. 51. Quaternions, Interpolation, and Animation (3 cr.)Prerequisite(s): MAT 3.

This course gives an introduction to several mathematical. Topics covered may. Clifford algebras. MAT 3. 52. Wavelets (3 cr.)Prerequisite(s): MAT 2. MAT 2. 58. This course presents the foundations of wavelets as a method. It discusses. background material in complex linear algebra and Fourier. Basic material on the discrete and continuous wavelet.

This includes the. Haar transform, and multi- resolution analysis. Other topics. may include subdivision curves and surfaces, and B- spline.

Applications to computer graphics may include image. D simulation problems. MAT 3. 53. Differential Geometry (3 cr.)Prerequisite(s): MAT 3. This course presents an introduction to differential geometry. Topics covered include parameterized. Gaussian curvature, the.

Gauss map, and an introduction to the intrinsic geometry. Other topics may include an introduction to. Riemannian geometry, and the. MAT 3. 54. Discrete and Computational Geometry (3 cr.)Prerequisite(s): MAT 2. MAT 2. 58. Topics covered in this course include convex hulls. Art Gallery theorems, Voronoi diagrams.

Delaunay graphs, Minkowski sums, path finding. Throughout the course, students explore. The analysis of these.

Although CS. 3. 30 is not a prerequisite, it is recommended. MAT 3. 55. Graph Theory (3 cr.)Prerequisite(s): MAT 2. MAT 2. 58. This course provides an introduction to the basic theorems and.

Topics include graph isomorphism. Euler tours, Hamiltonian cycles, and. Further topics may include spanning.

Applications may include network flows, graphical. The first course in differential.

This course builds upon these ideas with. Topics include. qualitative theory, dynamical systems, calculus of variations. Further topics may.