CS4310 Sensory Artificial Intelligence

A study of methods of computational simulation in natural-language processing, computer vision, and sensor networks. Issues in natural-language processing include modeling of syntax, semantics, morphology, discourse, phonetics, and stochastic phenomena. Issues in computer vision include low-level processing, segmentation, shape inference, and object identification. Issues in sensor networks include deployment, local inference, and communications.



Lecture Hours


Lab Hours


Course Learning Outcomes

Upon completion of this course the student is expected to:

  • Understand and describe key algorithms for the following:
    • Natural-language understanding by computer, including syntax, semantics, neural networks, large language models, generative methods
    • Speech understanding and signal interpretation
    • Computer vision, both low-level and high-level
    • Sensor networks and their management
  • Discuss the advantages and disadvantages of each of the above algorithms.
  • Be able to simulate important algorithms on paper.
  • Be able to implement key algorithms in an appropriate programming language.