• COSC 494: Spec Top: Computer Science

  1.00 - 3.00 Credits

  The University of Tennessee - Knoxville

  
  Repeatability: May be repeated. Maximum 9 hours.
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• COSC 501: Project in Lieu of Thesis

  3.00 Credits

  The University of Tennessee - Knoxville

  
  Capstone course taken under supervision of student’s major professor and master’s committee. Individual project involving literature survey, development of some algorithms, software, testing, writing a white paper or journal paper, or other suitable project. Repeatability: May be repeated. Maximum 6 hours.Registration Permission: Consent of graduate committee.
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• COSC 505: Intro/Prgrmng for Scient/Engrs

  3.00 Credits

  The University of Tennessee - Knoxville

  
  Introduce programming and computational science and engineering to graduate students in the sciences and engineering. Problem solving and algorithm development. Might use various programming languages such as C++, Python or others as needed.Comment(s): EECS majors cannot use course to meet degree requirements.Registration Restriction: Minimum student level – graduate.
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• COSC 522: Machine Learning

  3.00 Credits

  The University of Tennessee - Knoxville

  
  Theoretical and practical aspects of machine learning techniques related to pattern recognition. Statistical methods studied include Bayesian and linear classifiers, support vector machines, neural networks, and unsupervised learning. Syntactic methods include grammatical inference, string matching and Markov chains. Ensemble methods include random forests, adaptive boosting, and classifier fusion.Recommended Background: Programming, probability theory, linear algebra.
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• COSC 523: Artificial Intelligence

  3.00 Credits

  The University of Tennessee - Knoxville

  
  Theoretical and applied aspects of artificial intelligence. Course topics include problem solving and search, knowledge representation and reasoning, decision-making under uncertainty, machine learning, and multi-agent systems.Recommended Background: Programming, data structures and algorithms, linear algebra, probability theory.
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• COSC 524: Natural Language Processing

  3.00 Credits

  The University of Tennessee - Knoxville

  
  Presents natural language processing (NLP) theory and methods available for reasoning about text in computational systems. NLP is deeply interdisciplinary, drawing on both linguistics and computer science, and enables numerous applications of computational text analysis. Will cover major algorithms used in NLP, common applications of NLP, and the underlying linguistic phenomena NLP systems attempt to model.Recommended Background: Undergraduate coursework in algorithms and data structures.
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• COSC 525: Deep Learning

  3.00 Credits

  The University of Tennessee - Knoxville

  
  Theoretical and practical aspects of how to build deep networks for representations of high-dimensional data. Deep models for both supervised and unsupervised learning will be discussed, including convolutional neural network, autoencoder, generative adversarial network, and recurrent neural network.Recommended Background: Machine Learning and Python Programming Language.
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• COSC 526: Data Mining and Analytics

  3.00 Credits

  The University of Tennessee - Knoxville

  
  Provides a practical introduction to big data mining and analytics, blending theory and practice. Over the course of the semester, students will become familiar with modern data science methods, gain comfort with the tools of the trade, explore real-world data sets, and leverage the power of high performance and cloud resources to extract insights from data. Upon completing the course, students will learn how to create reproducible and explanatory data science workflows, how to implement a modern MapReduce, and how to implement parallel clustering methods. Students will also study strategies for overcoming the common imperfections in real-world datasets, and apply their new skills to extract insights from a high-dimensional dataset.Recommended Background: Python Programming.
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• COSC 527: Biolog-Inspired Computation

  3.00 Credits

  The University of Tennessee - Knoxville

  
  Recent developments in computational methods inspired by nature, such as neural networks, genetic algorithms, evolutionary programming, ant-swarm optimization, artificial immune systems, swarm intelligence, cellular automata, multi-agent systems, cooperation, and competition.
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• COSC 529: Autonomous Mobile Robots

  3.00 Credits

  The University of Tennessee - Knoxville

  
  Introduction to key artificial intelligence issues involved in the development of intelligent robotics. Methods studied include locomotion, navigation, sensing , localization, mapping, exploration, path planning, robot learning, uncertainty, and multi-robot systems.Recommended Background: 302, 311, Mathematics 251; and Mathematics 323 or Electrical and Computer Engineering 313.
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