Course Search Results

  • CS 2420: Intro Alg & Data Struct

    4.00 Credits
    University of Utah

    This course provides an introduction to the problem of engineering computational efficiency into programs. Students will learn about classical algorithms (including sorting, searching, and graph traversal), data structures (including stacks, queues, linked lists, trees, hash tables, and graphs), and analysis of program space and time requirements. Students will complete extensive programming exercises that require the application of elementary techniques from software engineering. Prerequisites: 'C-' or better in CS 1410 OR CS 1420 OR AP CS-A score of 5
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  • CS 2950: Independent Study

    1.00 - 4.00 Credits
    University of Utah

    No course description available.
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  • CS 3011: Industry Forum

    1.00 Credits
    University of Utah

    Presentations from local and national business leaders discussing issues in computing from industry perspectives, trends in computer science, professionalism, ethics, career readiness, lifelong learning, and contemporary issues. Offered on a graded basis. Prerequisites: Foundational Courses complete AND (Major or Minor in Kahlert School of Computing OR ECE)
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  • CS 3020: Research Forum

    1.00 Credits
    University of Utah

    Research Forum is a course with a format similar to that of CS 3011 Industry Forum, but with a focus on research. Throughout the semester, students will hear from a number of speakers about the kinds of problems that remain unsolved in computer science. The majority of the speakers to be School of computing faculty, with some speakers coming from outside of the university. Prerequisites: Foundational Courses complete AND (Major OR Minor in Kahlert School of Computing OR ECE)
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  • CS 3090: Ethics in Computing

    3.00 Credits
    University of Utah

    In this course, we will explore the moral, social, and ethical ramifications of the choices we make as computing professionals. Through class discussions, case studies, exercises, and projects, students will learn the basics of ethical thinking in science, understand a representative sample of current ethical dilemmas in computing, and study the distinct challenges associated with ethics in computing. Prerequisites: Foundational Courses complete AND (Major OR Minor in Kahlert School of Computing OR ECE)
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  • CS 3100: Models Of Computation

    3.00 Credits
    University of Utah

    This course covers different models of computation and how they relate to the understanding and better design of real-world computer programs. As examples, we will study Turing machines that help define the fundamental limits of computing, Push-down Automata that help build language parsers, and Finite Automata that help build string pattern matchers. This course also covers the basics of designing correctly functioning programs, and introduces the use of mathematical logic through Boolean satisfiability methods. The course will involve the use of hands-on programming exercises written at a sufficiently high level of abstraction that the connections between theory and practice are apparent. 'C-' or better in (CS2100 OR MATH2200) AND Foundational Courses complete AND (Major OR Minor in Kahlert School of Computing OR ECE)
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  • CS 3130: Eng Prob Stats

    3.00 Credits
    University of Utah

    An introduction to probability theory and statistics, with an emphasis on solving problems in electrical and computer engineering. Topics in probability include discrete and continuous random variables, probability distributions, sums and functions of random variables, the law of large numbers, and the central limit theorem. Topics in statistics include sample mean and variance, estimating distributions, correlation, regression, and hypothesis testing. Engineering applications include failure analysis, process control, communication systems, and speech recognition. Prerequisites: 'C-' or better in (MATH 1220 OR 1320 OR 1321 OR AP Calc BC score of 4+) AND Foundational Courses complete AND (Major OR Minor in Kahlert School of Computing OR ECE)
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  • CS 3190: Found. of Data Analysis

    3.00 Credits
    University of Utah

    This class will be an introduction to computational data analysis, focusing on the mathematical foundations. The goal will be to carefully develop and explore several core topics that form the backbone of modern data analysis topics, including Machine Learning, Data Mining, Artificial Intelligence, and Visualization. This will include some background in probability and linear algebra, and then various topics including Bayes' rule and connection to inference, gradient descent, linear regression and its polynomial and high dimensional extensions, principal component analysis and dimensionality reduction, as well as classification and clustering. We will also focus on modern models like PAC (probably approximately correct) and cross-validation for algorithm evaluation. Prerequisites:'C-' or better in(CS2100 OR MATH 2200)& (MATH2270 OR 2271)& Foundational Courses complete AND (Major OR Minor in Kahlert School of Computing OR ECE) Corequisites:'C-' or better in (MATH3070 OR CS3130 OR ECE3530)
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  • CS 3200: Intro Sci Comp

    3.00 Credits
    University of Utah

    Scientific and data computation relevant to computational science and engineering, with emphasis on the process of modeling, simulation, visualization and evaluation.This is an introduction to classical and modern computational and data science methods, with a focus on their theoretical and algorithmic development as well as their implementation. Topics may include numerical linear algebra, numerical approximation methods such as interpolation and linear regression, solving and linear and non-linear systems, and optimization. Basic knowledge of programming, matrices, and calculus is assumed. Recommended programming experience at the level of CS 2420 and Mathematical background at the level of integral calculus. Prerequisites: 'C-' or better in (MATH 2270 OR MATH 2271) AND Foundational Courses complete AND (Major OR Minor in Kahlert School of Computing OR ECE OR Physics)
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  • CS 3390: Ethics in Data Science

    3.00 Credits
    University of Utah

    In this course, we will explore the technical, social, and ethical ramifications of the choices we make at the different stages of the data analysis pipeline, from data collection and storage to understanding feedback loops in analysis. Through class discussions, case studies and exercises, students will learn the basics of ethical thinking in science, understand the history of ethical dilemmas in scientific work, and study the distinct challenges associated with ethics in modern data science. Prerequisites: 'C-' or better in (CS 2420 OR DS 2500) AND Foundational Courses complete AND (Major OR Minor in Kahlert School of Computing OR ECE OR Data Science Certificate status)
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