• COSC 424: Introduction to Deep Learning

  3.00 Credits

  The University of Tennessee - Knoxville

  
  An introductory course on the theoretical and practical aspects of how to build deep networks for representations of high-dimensional data. Deep models for both supervised and unsupervised learning, including convolutional neural networks, autoencoders, generative adversarial networks, and recurrent neural networks. (RE) Prerequisites: COSC 425 with a grade of C or better; COSC 302 or COSC 307 with a grade of C or better; MATH 251 or MATH 257 with a grade of C or better. Comment(s): Prior knowledge may satisfy prerequisites with consent of instructor.
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• COSC 425: Intro/Machine Learning

  3.00 Credits

  The University of Tennessee - Knoxville

  
  Machine learning is concerned with computer programs that automatically improve their performance through experience. This course covers the theory and practice of machine learning from a variety of perspectives. We cover topics such as learning decision trees, neural network learning, statistical learning methods, genetic algorithms, Bayesian learning methods, explanation-based learning, and reinforcement learning. Programming assignments include hands-on experiments with various learning algorithms.(RE) Prerequisite(s): 302; Electrical and Computer Engineering 313 or Mathematics 323. Comment(s): Prior knowledge may satisfy prerequisite with consent of instructor.
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• COSC 426: Intro to Data Mining/Analytics

  3.00 Credits

  The University of Tennessee - Knoxville

  
  Modern data science methods, tools of the trade, real-world data sets, and leveraging the power of high performance and cloud resources to extract insights from data. Upon completing the course, students will learn to create reproducible and explanatory data science workflows, to implement parallel clustering methods, to address imperfections in real-world datasets, and to extract insights from a high-dimensional dataset.(RE) Prerequisites: COSC 302 or COSC 307 with a grade of C or better; MATH 251 or MATH 257 with a grade of C or better; and ECE 313 or ECE317 or MATH 323 with a grade of C or better. Comment(s): Prior knowledge may satisfy prerequisites with consent of instructor.
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• COSC 427: Hon: Biol-Inspired Computation

  3.00 Credits

  The University of Tennessee - Knoxville

  
  Same as COSC 420 with additional honors project.Recommended Background: Completion of core courses.
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• COSC 429: SpecTop: AI & Machine Learning

  3.00 Credits

  The University of Tennessee - Knoxville

  
  Advanced topics of current interest in the area of artificial intelligence and machine learning.Repeatability: Maybe repeated but on a different special topic. Maximum 6 hours.(RE) Prerequisite(s): COSC 425 with a grade of C or better.
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• COSC 434: Network Security

  3.00 Credits

  The University of Tennessee - Knoxville

  
  In-depth introduction to core Internet and wireless technologies, related security concerns, common security vulnerabilities, and good security practices. Hands-on experience exploiting network protocols and communications, and setting up secure network connections.(RE) Prerequisite(s): Electrical and Computer Engineering 453, Electrical and Computer Engineering 461.
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• COSC 440: Formal Found/Software Engr

  4.00 Credits

  The University of Tennessee - Knoxville

  
  Principles of analysis and design of information systems. Principles of program design and verification, formal objects, formal specifications.Contact Hour Distribution: 3 hours lecture and 1 lab.(RE) Prerequisite(s): 311.
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• COSC 445: Fund of Digital Archeology

  3.00 Credits

  The University of Tennessee - Knoxville

  
  This is an advanced topic course focused on developing multi-disciplinary skills of discovering, retrieving, analyzing, and presenting operational data. Students will use critical thinking and intense practice solving real-world problems to recognize and address key operational issues: the lack of context, missing observations, and incorrect values. At the end of the course students will be able to discover operational data, to retrieve and store it, to recover context, to estimate the impact of missing events, to identify unreliable or incorrect values, and to present the results.Recommended Background: 340, 370, Electrical and Computer Engineering 313.
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• COSC 452: Computer Graphics

  3.00 Credits

  The University of Tennessee - Knoxville

  
  Digital image synthesis, geometric modeling, and animation. Topics may include visual perception, displays and color spaces, frame buffers, affine transformations, data structures for geometric primitives, visible surface determination, shading and texturing, anti-aliasing computing light transport, rendering equation, shader programming, general purpose GPU programming, level of detail, curves and surfaces, and graphics hardware.(RE) Prerequisite(s): 302 or 307.Comment(s): Prior knowledge may satisfy prerequisite with consent of instructor.
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• COSC 453: Data Visualization

  3.00 Credits

  The University of Tennessee - Knoxville

  
  The goal of this course is to develop a broad understanding of the principles, methods, and techniques for designing effective data visualizations. The course will span a wide range of topics related to interactive data visualization. The course will teach key elements of scientific visualization techniques, which graphically encode data with some physical or geometric correspondence, and information visualization techniques, which focus on abstract data without such correspondences such as symbolic, tabular, networked, hierarchical, or textual information sources. The course will follow a lecture/seminar style with discussion of assigned readings, as well as viewing of videos and hands-on experience with creating visualization tools.(RE) Prerequisite(s): 140.Recommended Background: 340 and 452.
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