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Faith Group

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Levi Clark
Levi Clark

Introduction To The Basic Concepts Of Modern Ph... !FREE!



Learn radar principles, systems, techniques, phenomenology, and the basics of radar technology. Get up-to-date examples of modern radar systems, including microwave and millimeter-wave, and their applications. Understand antennas, transmitters, receivers, signal processors, clutter and noise, detection, signal processing, waveform design, Doppler techniques, resolution, multipath, and reflectivity measurements.




Introduction to the Basic Concepts of Modern Ph...



PH 101 is our first non-calculus introduction to physics, and is aimed at students who desire (or require) a good working physics background, but will not necessarily continue into upper-level physics courses. Laboratory experiments will augment lecture- and discussion-based learning, and introduce students to key experimental techniques and analysis. The course will stress a conceptual (but less mathematically rigorous) understanding of everyday phenomena in terms of their basic underlying physical principles. Broadly, the course material can be grouped into the following areas:


PH 102 continues our non-calculus introduction to physics and includes electricity and magnetism, optics, and modern physics (i.e., relativity, quantum, atomic, and nuclear physics). Laboratory experiments will augment lecture- and discussion-based learning, and introduce students to key experimental techniques and analysis. The course will stress a conceptual (but less mathematically rigorous) understanding of everyday phenomena and recent technologies in terms of their basic underlying physical principles. Broadly, the course material can be grouped into the following areas:


PH 105 is our first calculus-based introduction to physics and is aimed at students who desire (or require) a detailed working physics background, particularly calculations and problem-solving. Laboratory experiments will augment lecture- and discussion-based learning, and introduce students to key experimental techniques and analysis. The course will stress a conceptual and mathematical understanding of everyday phenomena in terms of their basic underlying physical principles. Broadly, the course material can be grouped into the following areas:


PH 106 continues our calculus-based introduction to physics and is aimed at students who desire (or require) a detailed working physics background, particularly calculations and problem-solving. Laboratory experiments will augment lecture- and discussion-based learning, and introduce students to key experimental techniques and analysis. The course will stress a conceptual and mathematical understanding of everyday phenomena and recent technologies in terms of their basic underlying physical principles. Broadly, the course material can be grouped into the following areas:


Description and course materials: Lecture series on current topics in physics. Open to all undergraduates, aimed at students just starting their university education who want a broad introduction to exciting developments in modern physics at an introductory level. Faculty will present introductions to recent developments in physics, including student-suggested topics. The course will stress a conceptual understanding of everything from fundamental phenomena to recent technologies in terms of their basic underlying physical principles. Students present short research seminars on a topic of their choice once per semester.


This is the course for physics majors, honors students, and those of you that enjoy a challenge. PH 125 is our first honors calculus-based introduction to physics and is aimed at students who really, really want to know how things work. Laboratory experiments will augment lecture- and discussion-based learning, and introduce students to key experimental techniques and analysis. The course will stress a conceptual and mathematically rigorous understanding of everyday phenomena in terms of their basic underlying physical principles. Broadly, the course material can be grouped into the following areas:


This is the course for physics majors, honors students, and those of you that enjoy a challenge. PH 126 is our second honors calculus-based introduction to physics and is aimed at students who really, really want to know how things work. Laboratory experiments will augment lecture- and discussion-based learning, and introduce students to key experimental techniques and analysis. The course will stress a conceptual and mathematically rigorous understanding of everyday phenomena in terms of their basic underlying physical principles. Broadly, the course material can be grouped into the following areas:


Description and course materials: PH 253 is a study of topics in modern physics, including special relativity, quantum physics, atomic and nuclear structure, and solid state physics. Modern Physics refers to the developments in physics beginning with the revolutionary work of Einstein, Planck, Bohr, and others. The basic principles of special relativity and quantum mechanics will be taught with illustrations drawn from reaction kinematics in high energy collisions, particle accelerators, and medical imaging devices, atomic and molecular properties, and the electrical and thermal characteristics of liquids and solids. The course will conclude with a survey of what is currently known about nuclei and elementary particles and their role in cosmology and stellar evolution.


Description and course materials: The course provides an introduction to the topics of modern physics based on a theoretical approach. Topics include: the theory of special and general relativity with applications to black holes and cosmological models; particle physics and basic aspects of the standard model; nuclear physics with applications; fundamental interactions and symmetries; astrophysics of stellar evolution and celestial objects.


Quantum mechanics provides a basis for the study of most modern subjects in physics including quantum field theory, astrophysics, elementary particle physics and condensed matter to name a few. This course and its graduate level extension provide students of physics and astronomy with the necessary tools to understand and utilize the concepts of modern physics.


Econ 671 and 672 form the basic required sequence in econometrics for all doctoral students. Their purpose is to provide Ph.D. students with the training needed to do the basic quantitative analysis generally understood to be part of the background of all modern economists. This includes: the theory and practice of testing hypotheses, statistical estimation theory, the basic statistical theory underlying the linear model, an introduction to econometric methods, and the nature of the difficulties which arise in applying statistical procedures to economic research problems. (3 Credits)


This course surveys some of the tools and frameworks currently popular among data scientists and machine learning practitioners in academia and industry. The first half of the course consists of an accelerated introduction to the Python programming language, including brief introductions to object-oriented and functional programming styles as well as tools for code optimization. The second half of the course will survey tools for handling structured data (regular expressions, HTML/JSON, databases), data visualization, numerical and symbolic computing, interacting with the UNIX/Linux command line, and large-scale distributed computing. Several modern inferential techniques arising in machine learning and applied statistics will be reviewed. (3 credits)


This course provides basic concepts and several modern techniques of Bayesian modeling and computation. Foundational topics include decision theoretic characterization of Bayesian inference and its relation to frequentist methods, de Finetti-type theorems and the existence of priors, conjugate priors and other notions of objective prior distributions, and Bayesian model selection. The course covers a number of advanced modeling techniques, both classical and modern, which belong to the class of hierarchical models, spatiotemporal models, dynamics models and Bayesian nonparametric models. A substantial part of the course is devoted to computational algorithms based on Markov Chain Monte Carlo sampling for complex models, sequential Monte Carlo methods, and deterministic methods such as variational approximation. A key component of the course would involve data analysis with Bayesian techniques.


This is an advanced introduction to regression modeling and prediction, including traditional and modern computationally-intensive methods. The following topics will be covered: (1) Theory and practice of linear models, including the relevant distribution theory, estimation, confidence and prediction intervals, testing, model and variable selection, generalized least squares, robust fitting, and diagnostics; (2) Generalized linear models, including likelihood formulation, estimation and inference, diagnostics, and analysis of deviance; and (3) Large and small-sample inference as well as inference via the bootstrap, cross-validation, and permutation tests. (4 Credits)


This course is an introduction to Monte Carlo sampling and integration methods that arise in statistics. Course topics include: basic Monte Carlo methods (random number generators, variance reduction techniques, importance sampling and its generalizations), an introduction to Markov chains and Markov Chain Monte Carlo (Metropolis-Hastings and Gibbs samplers, data-augmentation techniques, convergence diagnostics). Optional topics include: sequential Monte Carlo, Hamiltonian Monte Carlo, advanced computational methods (approximate Bayesian computation, variational inference) for complex statistical models such as latent variable and hierarchical or nonparametric Bayesian models. (3 Credits)


This is an advanced introduction to regression modeling and prediction, including traditional and modern computationally-intensive methods. It includes a comprehensive treatment of linear models for independent observations using least squares estimation; non least-squares approaches including penalization methods for variable selection; regression methods for dependent data, including generalized least squares, estimating equations, and mixed models; generalized linear models and generalized estimating equations; quantile regression, dimension reduction regression, and smoothing-based methods. It also covers issues related to data collection, study design, and interpretation of findings, including missing data, non-representative samples, causality, and designed experiments. (4 credits). 041b061a72


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