Biologically based machine learning paradigms: an introductory course

ABSTRACT

This paper describes an introductory course on biologically based sub-symbolic machine learning paradigms. Specifically, this paper covers Artificial Neural Networks, Genetic Algorithms and Genetics-Based Machine Learning. It provides the structure, motivation, content, texts and tools for the course. This course is suitable for an upper division undergraduate level course or as an introductory graduate course. The paper includes a section on bibliographical references to aid the instructor in preparing for this course.

REFERENCES

• 1: Abunawass, A. M., & Maki, W. S. (1989). Cumulative negative transfer during successive training: Analysis of a second sequential learning problem. Proceedings of the International Joint Conference on Neural Networks, 2, pp 623.
• 2: Abunawass, A. M., Bukhres, O., Fisher, T. G., & Magel, K. (1990). Proceedings of the 21st SIGCSE technical symposium, pp 240-244.
• 3: James A. Anderson , Edward Rosenfeld, Neurocomputing: foundations of research, MIT Press, Cambridge, MA, 1988
• 4: Booker, L. (1987). Improving search in genetic algorithms. In L. Davis (ed.) Genetic Algorithms and Simulated Annealing. London: Pitman Publishers.
• 5: Ralph M. Butler , Roger E. Eggen , Susan R. Wallace, Introducing parallel processing at the undergraduate level, ACM SIGCSE Bulletin, v.20 n.1, p.63-67, Feb. 1988
• 6: Maureen Caudill, Neural networks primer, part I, AI Expert, v.2 n.12, p.46-52, Dec. 1987
• 7: Davis, L. (ed.) (1991). Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold.
• 8: Peter J. Denning , D. E. Comer , David Gries , Michael C. Mulder , Allen Tucker , A. Joe Turner , Paul R. Young, Computing as a discipline, Communications of the ACM, v.32 n.1, p.9-23, Jan. 1989 [doi>10.1145/63238.63239]
• 9: Edelman, G. (1988). Neural Darwinism. New York: Basic Books.
• 10: Ficek, R. (1991). Genetics-Based Machine Learning: Classifier Systems in the Computer Science Curriculum. Proceedings of the 24th Annual Small College Computing Symposium. pp 207-212.
• 11: Stephan I. Gallant, Connectionist expert systems, Communications of the ACM, v.31 n.2, p.152-169, Feb. 1988 [doi>10.1145/42372.42377]
• 12: David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1989
• 13: Harp, S., & Samad, T. (1991). Genetic synthesis of neural network architecture, in L. Davis (ed.), Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold, pp. 202-221.
• 14: Steven A. Harp , Tariq Samad , Aloke Guha, Designing application-specific neural networks using the genetic algorithm, Advances in neural information processing systems 2, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1990
• 15: Steven Alex Harp , Tariq Samad , Aloke Guha, Towards the genetic synthesis of neural network, Proceedings of the third international conference on Genetic algorithms, p.360-369, December 1989, George Mason University, United States
• 16: Hebb, D. O. (1949). The Organization of Behavior, New York: Wiley.
• 17: G. E. Hinton , T. J. Sejnowski, Learning and relearning in Boltzmann machines, Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations, MIT Press, Cambridge, MA, 1986
• 18: John H. Holland, Adaptation in natural and artificial systems, MIT Press, Cambridge, MA, 1992
• 19: John H. Holland, Genetic algorithms and classifier systems: foundations and future directions, Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application, p.82-89, October 1987, Cambridge, Massachusetts, United States
• 20: Hopfield, J. (1984). Neurons With Graded Response Have Collective Computational Properties. Proceedings National Academy of Science; 81: pp 3088-3092
• 21: T. Kohonen, Self-organization and associative memory: 3rd edition, Springer-Verlag New York, Inc., New York, NY, 1989
• 22: Maki, W. S., & Abunawass, A. M. (1991). A connectionist approach to conditional discrimination: Learning, short-term memory, and attention. In M. L. Commons, S. Grossberg, & J. E. R. Staddon (Eds.), Quantitative analysis of behavior: Neural network models of conditioning and action (pp 241-278). Hillsdale, NJ: Lawrence Erlbaum Associates.
• 23: James L. McClelland , David E. Rumelhart, Explorations in parallel distributed processing: a handbook of models, programs, and exercises, MIT Press, Cambridge, MA, 1989
• 24: James L. McClelland , David E. Rumelhart , CORPORATE PDP Research Group, Parallel distributed processing: explorations in the microstructure, vol. 2: psychological and biological models, MIT Press, Cambridge, MA, 1986
• 25: McCloskey, M., & Cohen, N. J. (1989). Catastrophic interference in connectionist networks: The sequential learning problem. The Psychology of Learning and Motivation, 24, pp 109-165.
• 26: Perceptrons: An Introduction to Computational Geometry, The MIT Press, 1969
• 27: Montana, D. J., & Davis, L. (1989). Training feeAforward neural networks using genetic algorithms. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, pp. 762-767.
• 28: Chris Nevison, An undergraduate parallel processing laboratory, ACM SIGCSE Bulletin, v.20 n.1, p.68-72, Feb. 1988
• 29: Rick L. Riolo, Bucket brigade performance: I. Long sequences of classifiers, Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application, p.184-195, October 1987, Cambridge, Massachusetts, United States
• 30: Rick L. Riolo, Bucket brigade performance: II. Default hierarchies, Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application, p.196-201, October 1987, Cambridge, Massachusetts, United States
• 31: J. L. McClelland , D. E. Rumelhart , G. E. Hinton, The appeal of parallel distributed processing, Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations, MIT Press, Cambridge, MA, 1986
• 32: D. E. Rumelhart , G. E. Hinton , R. J. Williams, Learning internal representations by error propagation, Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations, MIT Press, Cambridge, MA, 1986
• 33: Rumelhart, D. E., Hinton, G. E., & Willtiams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323,533-536.
• 34: Schneider, W. (1987). Session I presidential address: Connectionism: Is it a paradigm shift for psychology?. Behavior Research Methods, Instruments, & Computers, 2, pp 73-83.
• 35: Sejnowski, T., & Rosenberg, C., (1987) Parallel networks that learn to pronounce English text. Complex Systems, 1, 145-168.
• 36: N. E. Sondak , V. K. Sondak, Neural networks and artificial intelligence, Proceedings of the twentieth SIGCSE technical symposium on Computer science education, p.241-245, February 23-24, 1989, Louisville, Kentucky, United States
• 37: Special Issue on Artificial Neural Systems. Computer, March 1988.
• 38: The Association of the Computing Machinery Curricula Recommendation for Computer Science, Vol I, New York NY, 1979.
• 39: P. D. Wasserman, Neural computing: theory and practice, Van Nostrand Reinhold Co., New York, NY, 1989
• 40: George M. Whitson, An introduction to the parallel distributed processing model of cognition and some examples of how it is changing the teaching of artificial intelligence, ACM SIGCSE Bulletin, v.20 n.1, p.59-62, Feb. 1988
• 41: Will, C. A. (1988). DARPA neural network study (review). Neural Networks Reviews, 2, pp 74-102.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Connectionism and neural nets

Additional Classification:
  H. Information Systems
  H.1 MODELS AND PRINCIPLES

  K. Computing Milieux
  K.3 COMPUTERS AND EDUCATION
      K.3.2 Computer and Information Science Education
          Subjects: Curriculum; Computer science education

General Terms:
Human Factors