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  • EN 550.314: Actuarial Preparation

    1.00 Credits
    Johns Hopkins University

    Prereqs: 550.310, 550.311, or 550.420 This is a problem-solving course focused on preparing for Actuarial Exam P.
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  • EN 550.362: Intro to Optimization II

    4.00 Credits
    Johns Hopkins University

    Prerequisites: 550.361 and multivariable calculus An introductory survey of optimization methods, supporting mathematical theory and concepts, and application to problems of planning, design, prediction, estimation, and control in engineering, management, and science. Study of varied optimization techniques including linear programming, network-problem methods, dynamic programming, integer programming, and nonlinear programming. Appropriate for undergraduate and graduate students without the mathematical background required for 550.661.
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  • EN 550.371: Cryptology And Coding

    4.00 Credits
    Johns Hopkins University

    Prerequisites: EN.550.171 (AS.110.204 with permission of instructor), linear algebra, computing experience. A first course in the mathematical theory of secure and reliable electronic communication. Cryptology is the study of secure communication: How can we ensure the privacy of messages? Coding theory studies how to make communication reliable: How can messages be sent over noisy lines? Topics include finite field arithmetic, error-detecting and error-correcting codes, data compressions, ciphers, one-time pads, the Enigma machine, one-way functions, discrete logarithm, primality testing, secret key exchange, public key cryptosystems, digital signatures, and key escrow
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  • EN 550.386: Sci Computing:Diff Equat

    4.00 Credits
    Johns Hopkins University

    Prerequisites: Calculus III, and 550.291 or approved alternative (e.g.,110.201) A first course on computational differential equations and applications. Topics include floating-point arithmetic, algorithms and convergence, root-finding (midpoint, Newton, and secant methods), numerical differentiation and integration, and numerical solution of initial value problems (Runge–Kutta, multistep, extrapolation methods, stability, implicit methods, and stiffness). Theoretical topics such as existence, uniqueness, and stability of solutions to initial-value problems, conversion of higher order/ non-autonomous equations to systems, etc., will be covered as needed. Matlab is used to solve all numerical exercises; no previous experience with computer programming is required.
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  • EN 550.400: Mathematical Modeling and Consulting

    4.00 Credits
    Johns Hopkins University

    Prerequisites: ( EN.550.310 or EN.550.311 or EN.550.420 or EN.550.430 ) Creating, analyzing and evaluating statistical and mathematical models using case studies and real world datasets. Project-oriented practice and guidance in modeling techniques, with emphasis on communication of methods and results. Examples drawn from finance, environmental science and and medicine illustrate the data analysis process using exploratory data analysis and standard statistical modeling techniques. Topics include linear and generalized linear models, models for categorical data and time series models. Computation will be emphasized throughout using the R platform.
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  • EN 550.420: Intro To Probability

    4.00 Credits
    Johns Hopkins University

    Prereq: (110.106 or 110.108) and (110.107 or 110.109) or AP Credit for Calculus I and II Recommended corequisite: multivariable calculus. Probability and its applications, at the calculus level. Emphasis on techniques of application rather than on rigorous mathematical demonstration. Probability, combinatorial probability, random variables, distribution functions, important probability distributions, independence, conditional probability, moments, covariance and correlation, limit theorems. Students initiating graduate work in probability or statistics should enroll in 550.620. Students can use any of the 6th, 7th or 8th editions of the textbook.
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  • EN 550.426: Introduction to Stochastic Processes

    4.00 Credits
    Johns Hopkins University

    Mathematical theory of stochastic processes. Emphasis on deriving the dependence relations, statistical properties, and sample path behavior including random walks, Markov chains (both discrete and continuous time), Poisson processes, martingales, and Brownian motion. Applications that illuminate the theory.
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  • EN 550.430: Intro to Statistics

    4.00 Credits
    Johns Hopkins University

    Prerequisite: 550.420 or approved alternative. Introduction to the basic principles of statistical reasoning and data analysis. Emphasis on techniques of application. Classical parametric estimation, hypothesis testing, and multiple decision problems; linear models, analysis of variance, and regression; nonparametric and robust procedures; decision-theoretic setting, Bayesian methods.
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  • EN 550.431: Statistical Methods in Imaging

    3.00 Credits
    Johns Hopkins University

    Prereq: (110.202 or 110.211) and (550.310 or 550.311 or 550.420) Denoising, segmentation, texture modeling, tracking, object recognition are challenging problems in imaging. We will present a collection of statistical models and methods in order to address these, including the E.M. algorithm, Maximum Entropy Modeling, Particle filtering, Markov Random Fields and Belief Propagation. Co-listed with 580.466.
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  • EN 550.439: Time Series Analysis

    4.00 Credits
    Johns Hopkins University

    Prereqs: 550.310, 550.311, or equivalent calculus-based probability course, 110.201 or 550.291 and mathematical maturity Time series analysis from the frequency and time domain approaches. Descriptive techniques; regression analysis; trends, smoothing, prediction; linear systems; serial correlation; stationary processes; spectral analysis.
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