CollegeTransfer.Net

Course Criteria

Add courses to your favorites to save, share, and find your best transfer school.

  • MATH 8650: Data Structures

    3.00 Credits
    Clemson University

    Representation and transformation of information; formal description of processes and data structures; tree and list structures; pushdown stacks; string and formula manipulation; hashing techniques; interrelation between data structure and program structure; storage allocation methods. Offered fall semester only. Students are expected to have computational maturity before enrolling in this course.
    ShareMATH 8650 - Data Structures
    Favorite

  • MATH 8660: Finite Element Method

    3.00 Credits
    Clemson University

    Discusses the basic theory of the finite element method (FEM) for the numerical approximation of partial differential equations. Topics include Sobolev spaces, interpolation theory, finite element spaces, error estimation, and implementation of FEM in one and higher dimensions. Students are expected to have completed a senior- or graduate-level course in scientific computing and an undergraduate-level course in advanced calculus II; or to have completed an undergraduate-level course in advanced calculus I and a graduate-level course in matrix analysis before enrolling in this course.
    ShareMATH 8660 - Finite Element Method
    Favorite

  • MATH 8710: Machine Learning I

    3.00 Credits
    Clemson University

    Machine learning studies statistical models and associated algorithms used to perform tasks automatically with limited human instructions. This course is the first of a two-semester sequence on machine learning. This course introduces the basics on machine learning models, related optimization algorithms, and their applications.
    ShareMATH 8710 - Machine Learning I
    Favorite

  • MATH 8720: Machine Learning II

    3.00 Credits
    Clemson University

    Machine learning studies statistical models and associated algorithms used to perform tasks automatically with limited human instructions. This course is the second in a two-semester sequence on machine learning. This course covers recent advances on machine learning, including statistical models, learning theory, optimization algorithms, and their applications. Preq: MATH 8710 or consent of instructor.
    ShareMATH 8720 - Machine Learning II
    Favorite

  • MATH 8740: Integration Thrgh Optimization

    3.00 Credits
    Clemson University

    Theory, methodology and applications of decomposition, integration and coordination for large-scale or complex optimization problems encountered in engineering design. Topics include conventional and non-conventional engineering optimization algorithms, analysis models and methods, multidisciplinary optimization, analytic target cascading, multiscenario optimization, and multicriteria optimization. Case studies are included. May also be offered as ME 8740. Students are expected to have completed a graduate-level course in mathematical programming or scientific computing or engineering optimization before enrolling in this course.
    ShareMATH 8740 - Integration Thrgh Optimization
    Favorite

  • MATH 8810: Mathematical Statistics

    3.00 Credits
    Clemson University

    Fundamental concepts of sufficiency, hypothesis testing and estimation; robust estimation; resampling (jackknife, bootstrap, etc.) methods; asymptotic theory; two-stage and sequential sampling problems; ranking and selection procedures. Offered spring semester only. Students are expected to have completed a course in statistical inference before enrolling in this course.
    ShareMATH 8810 - Mathematical Statistics
    Favorite

  • MATH 8820: Intro to Bayesian Statistics

    3.00 Credits
    Clemson University

    Selective course focused on Bayes theorem, conjugate priors, posterior distributions, credible intervals, Monte Carlo approximations, Markov chain Monte Carlo (MCMC) methods, Gibbs sampling, Metropolis-Hastings algorithm, Bayesian hypothesis testing, hierarchical modeling, linear regression, and logistic regression. Preq: Students are expected to have completed a course in introductory probability and a course in introductory statistics, and have some experience with the software R before enrolling in this course.
    ShareMATH 8820 - Intro to Bayesian Statistics
    Favorite

  • MATH 8840: Statistics for Experimenters

    3.00 Credits
    Clemson University

    Statistical methods for students who are conducting experiments; introduction to descriptive statistics, estimation and hypothesis testing as they relate to design of experiments; higher-order layouts, factorial and fractional factorial designs, and response surface models. Offered fall semester only. Students are expected to have completed a course in multivariable calculus before enrolling in this course.
    ShareMATH 8840 - Statistics for Experimenters
    Favorite

  • MATH 8850: Advanced Data Analysis

    3.00 Credits
    Clemson University

    Continuation of MATH 8050 covering alternatives to ordinary least squares, influence and diagnostic considerations, robustness, special statistical computation methods. Offered spring semester only. Students are expected to have completed a graduate-level regression analysis course before enrolling in this course.
    ShareMATH 8850 - Advanced Data Analysis
    Favorite

  • MATH 8910: Master's Thesis Research

    1.00 Credits
    Clemson University

    Students conduct master's thesis research.
    ShareMATH 8910 - Master's Thesis Research
    Favorite
To find college, community college and university courses by keyword, enter some or all of the following, then select the Search button.
(Type the name of a College, University, Exam, or Corporation)
(For example: Accounting, Psychology)
(For example: ACCT 101, where Course Prefix is ACCT, and Course Number is 101)
(For example: Introduction To Accounting)
(For example: Sine waves, Hemingway, or Impressionism)
Distance:
of
(For example: Find all institutions within 5 miles of the selected Zip Code)