Course Search Results

• STAT 6555: Advanced R Programming for Data Science

  3.00 Credits

  Utah State University

  
  This programming intensive course covers key tools and programming principles for conducting reproducible data analyses in the R programming language. Emphasis is placed on computational reproducibility and scalability as presented in the context of custom R package development. Additional coursework is required for those enrolled in the graduate-level course.
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• STAT 6560: Statistical Visualization II

  2.00 Credits

  Utah State University

  
  Students learn about statistical and scientific visualization of statistical maps and high-dimensional data; historic developments of graphics; current frontiers in visualization, including interactive, dynamic, and web-based graphics; and discuss effective use of color and motion in graphics. For graduate (6000-level) credit, a major project is required.
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• STAT 6570: Statistical Bioinformatics

  2.00 Credits

  Utah State University

  
  This course introduces statistical methods for high dimensional biomedical data, primarily gene expression and sequence analysis, using Bioconductor tools. Topics include data visualization, differential expression (in high-dimensional count/continuous data), annotation testing, scoring alignments, HMMs, and phylogenetic trees. For graduate (6000-level) credit additional work is required.
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• STAT 6645: Mathematical Methods for Data Science

  3.00 Credits

  Utah State University

  
  This course provides an in-depth overview of important mathematical principles and methods that underlie state-of-the art data science, statistical, and machine learning methods with a focus on linear algebra, multivariate calculus, and optimization and their data science applications. Additional coursework is required for those enrolled in the graduate-level course.
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• STAT 6650: Applied Machine Learning and Multivariate Statistics

  3.00 Credits

  Utah State University

  
  This broadly accessible course introduces students to several machine learning and multivariate statistics topics with a focus on real data applications. Topics include a range of approaches for classification, regression, dimensionality reduction, and cluster analysis. Additional coursework is required for those enrolled in the graduate-level course.
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• STAT 6655: Machine Learning

  3.00 Credits

  Utah State University

  
  This course provides a practical and mathematical introduction to machine learning techniques and principles in supervised and unsupervised settings. Students learn to understand machine learning research papers and gain the practical experience to implement machine learning approaches on real data. Additional coursework is required for those enrolled in the graduate-level course.
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• STAT 6680: Statistical Thinking for Big Data

  2.00 Credits

  Utah State University

  
  Students learn what Big Data is and how to visualize it. Topics include MapReduce, estimation and the bag of little bootstraps, and hypothesis testing, linear regression, classification, clustering, and nonlinear regression for Big Data.
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• STAT 6685: Deep Learning Theory and Applications

  3.00 Credits

  Utah State University

  
  This course takes a principled and hands-on approach to deep learning with neural networks, covering machine learning basics, backpropagation, stochastic gradient descent, regularization, and universality. Topics include CNNs, GANs, RNNs, GCNs, autoencoders, transformers, and other modern architectures and training techniques. Additional coursework is required for those enrolled in the graduate-level course.
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• STAT 6710: Mathematical Statistics I

  3.00 Credits

  Utah State University

  
  This course is an advanced mathematical statistics course covering the basics of measure-theoretic probability, random variables and random vectors, generating functions, and modes of stochastic convergence.
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• STAT 6720: Mathematical Statistics II

  3.00 Credits

  Utah State University

  
  This course is an advanced mathematical statistics course covering the basics of measure-theoretic probability, random variables and random vectors, generating functions, and modes of stochastic convergence.
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