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MLeXAI: A Project-Based Application-Oriented Model

Published:01 August 2010Publication History
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Abstract

Our approach to teaching introductory artificial intelligence (AI) unifies its diverse core topics through a theme of machine learning, and emphasizes how AI relates more broadly with computer science. Our work, funded by a grant from the National Science Foundation, involves the development, implementation, and testing of a suite of projects that can be closely integrated into a one-term AI course. Each project involves the development of a machine learning system in a specific application. These projects have been used in six different offerings over a three-year period at three different types of institutions. While we have presented a sample of the projects as well as limited preliminary experiences in other venues, this article presents the first assessment of our work over an extended period of three years. Results of assessment show that the projects were well received by the students. By using projects involving real-world applications we provided additional motivation for students. While illustrating core concepts, the projects introduced students to an important area in computer science, machine learning, thus motivating further study.

References

  1. Boyan, J. A. 1998. Learning evaluation functions for global optimization. Tech. rep. CMU-CS-98-152, Carnegie Mellon University.Google ScholarGoogle Scholar
  2. Cormen, T. H., Leiserson, C. E., Rivest, R. L., and Stein C. 2001. Introduction to Algorithms 2nd Ed. The MIT Press, Cambridge, MA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Dodds, Z. et al., (eds.) 2006. Robots and robotics in undergraduate AI education. AI Mag. 27, 1. AAAI Press.Google ScholarGoogle Scholar
  4. Fox, S. 2007. Introductory AI for both computer science and neuroscience students. In Proceedings of the 20th International FLAIRS Conference (FLAIRS’07).Google ScholarGoogle Scholar
  5. Greenwald, L., (ed.) 2004. Accessible hands-on artificial intelligence and robotics education. Tech. rep. AAAI Press.Google ScholarGoogle Scholar
  6. Harlan, R., Levine, D., and McClarigan, S. 2001. The Khepera robot and the kRobot class. In Proceedings of the 32nd SIGCSE Technical Symposium on Computer Science Education (SIGCSE’01). 105--109. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Hearst, M., ed. 1995. Improving instruction of introductory AI. Tech. rep. FS-94-05, AAAI Press.Google ScholarGoogle Scholar
  8. Hoos, H. H. and Stützle, T. 2005. Stochastic Local Search: Foundations and Applications. Morgan Kaufmann. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Klassner, F. 2006. Launching into AI’s October Sky with robotics and lisp. AI Mag. 27, 1. AAAI Press.Google ScholarGoogle Scholar
  10. Knizia, R. 1999. Dice Games Properly Explained. Elliot Right-Way Books, Lower Kingswood, UK. 129.Google ScholarGoogle Scholar
  11. Kumar, D. and Meeden, L. 1998. A robot laboratory for teaching artificial intelligence. In Proceedings of the 29th SIGCSE Technical Symposium on Computer Science Education (SIGCSE’98). 341--344. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Kumar, A., et al. 2006. Non-traditional projects in the undergraduate AI course. In Proceedings of the 37th Annual SIGCSE Technical Symposium on Computer Science Education (SIGCSE’06). Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Markov, Z. and Larose, D. 2007. Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage. Wiley, New York. (Chapter 1 is available for free download from http://media.wiley.com/product_data/excerpt/56/04716665/0471666556.pdf). Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Markov, Z., Russell, I., Neller, T., and Zlatareva, N. 2006b. Pedagogical possibilities for the N-puzzle problem. In Proceedings of the 36th Frontiers in Education Conference (FEC’06). IEEE Press.Google ScholarGoogle Scholar
  15. Mitchell, T. M. 1997. Machine Learning. McGraw-Hill, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Mitchell, T. M. 2006. The discipline of machine learning. Tech. rep. CMU-ML-06-108, Carnegie Mellon University.Google ScholarGoogle Scholar
  17. Moskewicz, M., Madigan, C., Zhao, Y., Zhang, L., and Malik, S. 2001. Chaff: Engineering an efficient sat solver. In Proceedings of the 39th Design Automation Conference (DAC’01). Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Neller, T., Presser C., Russell, I., and Markov, Z. 2006a. Pedagogical possibilities for the dice game Pig. J. Comput. Sci. Col. 21, 6, 149--161. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Nilsson, N. 1998. Artificial Intelligence: A New Synthesis. Morgan Kaufmann Publishers. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Russell, I. and Neller, T. 2003. Implementing the intelligent systems knowledge units of computing curricula 2001. In Proceedings of the Frontiers in Education Conference (FIE’03). IEEE Press.Google ScholarGoogle Scholar
  21. Russell, S. and Norvig, P. 2003. Artificial Intelligence: A Modern Approach. Prentice-Hall, Upper Saddle River, NJ. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Russell, I., Markov, Z., Neller, T., and Coleman, S. 2005a. Enhancing undergraduate AI courses through machine learning projects. In Proceedings of the 35th Frontiers in Education Conference (FIE’05). IEEE Press.Google ScholarGoogle Scholar
  23. Russell, I., Markov, Z., Neller, T., Georgiopoulos, M., and Coleman, S. 2005b. Unifying undergraduate AI courses through machine learning projects. In Proceedings of the 25th American Society for Engineering Education Conference (ASEE’05).Google ScholarGoogle Scholar
  24. Selman, B., Kautz, H., and Cohen, B. 1996. Local search strategies for satisfiability testing. In Proceedings of the DIMACS Series in Discrete Mathematics and Theoretical Computer Science, vol. 26 (DIMACS’96), 521--532.Google ScholarGoogle Scholar
  25. Sutton, R. S. and Barto, A. G. 1998. Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Weka Home Page. http://www.cs.waikato.ac.nz/~ml/weka.Google ScholarGoogle Scholar
  27. Wilensky, U. 1991. Abstract meditations on the concrete and concrete implications for mathematics education. In I. Harel and S. Papert, Eds. Constructionism. Ablex, Norwood, NJ. 193--203.Google ScholarGoogle Scholar
  28. Wyatt, R. 2000. Curriculum descant. ACM Intell. Mag. 11, 2. ACM Press.Google ScholarGoogle Scholar

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      • Published in

        cover image ACM Transactions on Computing Education
        ACM Transactions on Computing Education  Volume 10, Issue 3
        August 2010
        79 pages
        EISSN:1946-6226
        DOI:10.1145/1821996
        Issue’s Table of Contents

        Copyright © 2010 ACM

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        Publication History

        • Published: 1 August 2010
        • Accepted: 1 May 2010
        • Revised: 1 March 2010
        • Received: 1 August 2009
        Published in toce Volume 10, Issue 3

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