Database Management Systems (DBMSs are at the heart of modern commercial application development. Their use extends beyond this to many other environments and domains where large amounts of data must be stored for efficient update, retrieval, and analysis. In this course, we learn how to use DBMSs. Although the scope of this course is limited to existing DBMSs, you will also learn important concepts that will help you start working with next generation of DBMSs (such as NoSQL data stores) for Big Data management.
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This course covers the design and implementation of traditional relational database systems as well as advanced data management systems. The course will treat fundamental principles of databases such as the relational model, conceptual design, and schema refinement. It will also cover core database implementation issues including storage and indexing, query processing and optimization, and transaction management. In this course, we will also discuss challenges in modern data management systems, including an introduction to Big Data management systems.
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Introduces data mining concepts and techniques, including but not limited to data preprocessing, data warehousing, pattern mining, classification, prediction, cluster analysis, outlier detection, and online data analytics.
CData processing systems typically each address one particular common workload thereby bucking the conventional wisdom that you can build a data processing system for which "one size fits all". In this course will attempt to survey the wide array Big Data Systems including the following:
Unless you've been living in a cave, you know that many fields are faced with mountains of data that could present a unique opportunity if only it were possible to efficiently process all this information. This leads to the so-called "Big Data" challenge that has been getting lots of press, including a call to arms from the President. The database field has been addressing problems of scale for some time, but the exponential growth in data volumes over the last decade has even the traditional database providers stumped. In other words, the products that the big database firms have been selling are not up to the task. If you couple this with some interesting new hardware opportunities, you get a moment in time in which many new and radical approaches to database management systems have been proposed but the winners have not yet been determined. In this course, we will review and learn about these modern database engines, dubbed Data Stores.
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In this course, introductory data science concepts are mainly taught through lectures and class discussions on the selected complementary topics. In addition to lectures, students will learn how to apply the data scientific concepts they study through in-class labs as well as homework assignments that focus on the use and implementation of the data scientific techniques on real datasets from various application domains. Finally, to stimulate creativity of the students in the context of data science and to enhance their research skills, an individual or group of students will take on a term project, with which throughout the semester the student(s) will work toward introduction and implementation of novel solutions for their proposed (interesting and new) data science problem. A list of suggested project ideas will be provided but the students are allowed to introduce their own project ideas, if they choose to do so. As a result of the term project, the student(s) are expected to develop and demonstrate an interesting proof-of-concept data scientific system prototype, and will prepare a written and oral report explaining their innovation. See the course Canvas site to learn more about project options and requirements. Website:
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Infrequent. Develops mathematical reasoning: introduces linear algebra, discrete structures, graph theory, probability, and differential equations, using applications to molecular biology.
moreExamines elementary theory of probability, including independence, conditional probability, and Bayes theorem; random variables, expectations and probability distributions; joint and conditional distributions; functions of random variables; limit theorems, including the central limit theorem.
moreSampling distributions, maximum likelihood and method of moments estimation, properties of estimators, classical methods for confidence intervals and hypothesis testing, simple linear regression.
moreTopics include simple and multiple linear regression, model diagnostics and remediation, and model selection. Emphasis is on practical aspects and applications of linear models to the analysis of data in business, engineering and behavioral, biological and physical sciences.
moreInfrequent. Algorithmic techniques in graph theory and other discrete mathematics areas. Typical topics include: branch-bound algorithms, matching, colorings, domination, min-plus algebra, simulated annealing and related heuristics, NP-completeness theory. Prereq: a course in graph theory and some programming experience.
moreEvery other year. Basic introduction and mathematical foundations. Topics include comparative genomics; proteomics; phylogeny; dynamic programming and sequence alignment; gene expression arrays and clustering; Bayesian networks; structure prediction and hidden Markov models.
moreMethods and analysis of techniques used to resolve continuous mathematical problems on the computer. Solution of linear and nonlinear equations, interpolation and integration.
moreNumerical differentiation and integration, numerical solution of ordinary differential equations, and numerical solutions of partial differential equations as time allows.
moreEvery other year. Markov chains; Poisson processes, continuous time Markov chains, elementary topics in queuing theory, and some mathematical aspects of Monte Carlo simulation, including random variate generation, variance reduction, and output analysis.
moreReview of estimation, confidence intervals and hypothesis testing; ANOVA; categorical data analysis; non-parametric tests; linear and logistic regression.
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Introduces information theory and its application in computer science, communication theory, coding and applied mathematics. Entropy, mutual information, data compression and storage, channel capacity, rate distortion, hypothesis testing. Error detecting and correcting codes, block codes and sequential codes.
morePattern recognition techniques from image processing and artificial intelligence are explored. Topics include neural networks, morphological processing, wavelets, fractals, and basic image understanding.
moreAn important component of the recent expansion in biomedical engineering is the area of biomedical imaging. This ELEC 4644/5644 course is an introduction to biomedical imaging systems, not only covering the fundamentals of imaging physics but also the applications of four primary biomedical imaging modalities: X-Ray Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Nuclear Medicine (i.e. PET, SPECT), and Ultrasound Imaging.
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Provides an overview exposure and experience with various aspects of GIS technology and its uses for natural resource and infrastructure, planning, design and management. This course involves a survey of GIS software and hardware, review of cartographic mapping principles, hands-on applications to environmental impact assessment, municipal facilities management, transportation, water resources and demographics. GIS project management factors are addressed.
moreThis third course reviews GIS software functions and terminology, including data entry (input, editing), manipulation (projection, merge, window, aggregate), analysis (map algebra, overlay, Boolean, interpolation network, measurements, distance, terrain modeling, statistical analysis), query (spatial, attribute), and display/reporting. Integration of various domain-specific systems analysis models with GIS databases is also addressed. Laboratory activities involve programming applications using available GIS.
moreAddresses remote sensing concepts including 1) imaging sensors and geo-referencing; 2) image processing for radiometric, multi-spectral image enhancement, and multi-sensor image fusion; and 3) multi-spectral image classification, including feature extraction, supervised and unsupervised classification, and extensions to hyper-spectral data.
moreCovers statistical analysis methods for engineering studies in general, and for highway accident and traffic flow data in particular. Topics include data needs, sampling designs, survey methods, hypothesis testing, tests of proportions, non-parametric tests, analysis of variance, multivariate regression, and other tests of fit. Introductory overview of state and federal accident databases. Comparisons of accident rates by highway type, vehicle speeds, vehicle types, weather conditions and other factors also presented.
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This course provides mathematical tools essential for graduate level bioengineering work. Studies selected topics from probability, linear algebra, and vector calculus, with emphasis on bioengineering applications.
moreProvides computational skills and knowledge of numerical methods for engineering/scientific computation using Matlab. Topics: root finding, interpolation, difference and integration rules, solution of initial and boundary value ODEs, and introduction to the solution of PDEs.
moreThis course will prepare students fundamental bioengineering principles common to areas of active research. This includes fundamental principles behind systems and instrumentation in mechanics, electronics, fluid flow and clinical imaging modalities, as well as an introduction to polymeric biomaterials.
moreThis course covers MatLab programming for bioengineers and life scientists. Topics include MatLab syntax and optimization as well as techniques for working with scalars, time-series, images and multi-dimension datasets. Surface/Curve fitting, modeling, automation and classification will be covered as well.
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