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.

Prerequisite

CS3310

Lecture Hours

4

Lab Hours

1

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.