Artificial Intelligence and Cognitive Process (Fall 2011)
 
  • Interdisciplinary Program in Cognitive Science, Seoul National University
  • Instructor: Dr. Joon Shik Kim (Room: 302-314-1, E-mail: jskim@bi.snu.ac.kr, Tel: 880-1847, H.P.: 010-2838-0324)
  • TA: Chung-Yeon Lee (Room: 302-314-1, E-mail: cylee@bi.snu.ac.kr, Tel: 880-1847)
  • Classroom: 14-207-1
  • Time: Friday 09:00-12:00


  • Objectives
    • Mathematical and statistical modeling of intelligence
    • Study of an information processing system based on the masterpiece papers in early days
    • Study of inference algorithms of Markov chain Monte Carlo (MCMC)

  • Text book
    • Unsupervised Learning: Foundations of Neural Computation, edited by Geoffrey Hinton and Terrence J. Sejnowski, The MIT Press, 1999

  • Evaluation
    • Paper presentation (20%)
    • Research presentation I (15%)
    • Research presentation II (15%)
    • Report of a term project (30%)
    • Participation in discussion (20%)

  • Announcement
    • Presentation will be evaluated by other students

  • A term project
    • The project consists of performing a cognitive science experiment and writing a final report
    • Main reference for a project will be presented at the paper presentation class

  • References
    1. Computing machinery and intelligence, AM Turing, Mind, 59(236): 433-460, 1950.
    2. Preliminary discussion of the logical design of an electronic computing instrument, AW Burks, HH Goldstine, and J von Neumann, Papers of John von Neumann on computing and computer theory: MIT press, 97-142, 1987.
    3. A theory for archicortex, D Marr, Philosophical transactions of the royal society of London, 262: 3-81, 1971.
    4. Theory of communication, CE Shannon, ACM mobile communications review, 5(1): 3-55, 2001.
    5. A global minimization algorithm based on a geodesic of a Lagrangian formulation of Newtonian dynamics, JS Kim, JC Kim, J O, and BT Zhang, Neural Processing Letters, 26(2): 121-131, 2007.
    6. Markov chain sampling methods for Drichlet process mixture models, RM Neal, Journal of computational and graphical statistics, 9(2): 249-265, 2000.
    7. Hierarchical Dirichlet processes, YW Teh, MI Jordan, MJ Beal, DM Blei, Journal of the American statistical association, 101(476): 1566-1581, 2005.
    8. Z Ghahramani, TL Griffiths, and P Sollich, Bayesian nonparametric latent feature model, proc. Valencia / ISBA 8th world meeting on Bayesian statistics, June 1st-6th, 2006.
    9. NL Roux and Y Bengio, Representational power of restricted Boltzmann machines and deep belief networks, Neural Computation, 20: 1631-1649, 2008.
    10. An evolutionary Monte Carlo algorithm for predicting DNA hybridization, JS Kim, JW Lee, YK Noh, JY Park, DY Lee, KA Yang, YG Chai, JC Kim, and BT Zhang, Biosystems, 91(1):69-75, 2008.

  • Schedule
    •   Date Paper Speaker Slides
      9.2 Paper written by Turing (Ref. [1]) Joon Shik Kim
      9.9 Paper written by Neumann (Ref. [2]) Joon Shik Kim
      9.16 Paper written by Marr (Ref. [3]) Joon Shik Kim
      9.23 Paper written by Shannon (Ref. [4]) Joon Shik Kim
      9.30 Geodesic of a Lagrangian (Ref. [4]) Joon Shik Kim
      10.7 Paper Presentation Students  
      10.14 Markov chain sampling (Ref. [6]) Joon Shik Kim
      10.21 Hierachical Dirichlet process (Ref. [7]) Joon Shik Kim
      10.28 Indian buffet process (Ref. [8]) Joon Shik Kim
      11.4 Research Presentation I Students  
      11.11 Restricted Bolzmann machine (Ref. [9]) Joon Shik Kim
      11.18 DNA computing (Ref. [10]) Joon Shik Kim
      11.25 Research presentation II Students  
      12.2 Due day of the final report
Last updated: August 2011 by Chung-Yeon Lee