ECE 548 - Computer Models of Cognitive Processes

Semesters Offered

Official Description

Course Information: Same as CS 548. See CS 548.

Prerequisites

Credit in CS 440

Subject Area

Biomedical Imaging, Bioengineering, and Acoustics

Course Directors

Computer Science

Description

Formal models and concepts in vision and language; detailed analysis of computer vision, language, and learning problems; relevant psychological results and linguistic systems; and survey of the state of the art in artificial intelligence.

Notes

Same as CS 548.

Topics

  • Relevant psychological results in vision: the frog's eye; the cat's visual system; human visual phenomena; neural net models and neurophysiological results
  • Computer vision systems: adaptive systems; perceptrons; heuristic systems (Guzman, Chang, Agin, Binford); structured systems (Huffman, Clowes, Waltz); model-driven systems (Shirai, Tennenbaum)
  • Representation of visual information: pattern recognition and templates; polyhedra, line drawings, structural descriptions; natural objects and scenes with motion
  • Frame-systems: Is vision symbolic? The importance of context; cultural factors in perception, relationships between perception and language; reasoning
  • Linguistics: historical perspective and problems of human and machine translation; transformational grammars and syntax; augmented transition networks; systemic grammars, case grammars, and semantics
  • Computer language systems: analysis of programs by Weizenbaum, Bobrow, Quillian, Simmons, Woods, Schank, Winograd, Martin, Rumelhart and Norman
  • Current problems and research: learning and program meta-description (Piaget-Sussman); the natue of intelligence; language of chimpanzees; belief systems and abiguity (Charniak, McCarthy, Colgy)

Detailed Description and Outline

Topics:

  • Relevant psychological results in vision: the frog's eye; the cat's visual system; human visual phenomena; neural net models and neurophysiological results
  • Computer vision systems: adaptive systems; perceptrons; heuristic systems (Guzman, Chang, Agin, Binford); structured systems (Huffman, Clowes, Waltz); model-driven systems (Shirai, Tennenbaum)
  • Representation of visual information: pattern recognition and templates; polyhedra, line drawings, structural descriptions; natural objects and scenes with motion
  • Frame-systems: Is vision symbolic? The importance of context; cultural factors in perception, relationships between perception and language; reasoning
  • Linguistics: historical perspective and problems of human and machine translation; transformational grammars and syntax; augmented transition networks; systemic grammars, case grammars, and semantics
  • Computer language systems: analysis of programs by Weizenbaum, Bobrow, Quillian, Simmons, Woods, Schank, Winograd, Martin, Rumelhart and Norman
  • Current problems and research: learning and program meta-description (Piaget-Sussman); the natue of intelligence; language of chimpanzees; belief systems and abiguity (Charniak, McCarthy, Colgy)

Same as CS 548.

Texts

Jude Shavlik and Thomas Dietterich, Readings in Machine Learning, Morgan Kaufman.

Last updated

2/13/2013