• Instructor:Prof. Michael Gargano and Prof. Sung-Hyuk Cha

  • Meeting:

  • Course Goal:
    If you have a problem to solve, how should you approach its solution? In this course we will discuss the characteristics of a variety of problems, analyze their solutions and thus create an overview of a classification for the different solution methods. At each meeting we will discuss the rationale behind some solution tools available to the modern researcher. Through case studies and research papers, we will try to determine why a researcher chose and used a particular method and discuss its pro and cons. At the end of this course you will be better able to analyze your own research DPS thesis problem and have a good foundation for making a sound choice in deciding which research methods and tools are appropriate for you.

  • Textbook:
    1. How to Solve It: Modern Heuristics, Michalewicz, Z., Fogel, D.B., Springer, 2nd Ed., (2004).
    2. Data Mining: Practical Machine Learning Tools and Techniques, Witten I.H., Frank,E., Morgan Kaufmann, 2nd ed., ISBN 0-12-088407-0, (2005).
    1. An Introduction to Mathematical Taxonomy, Dunn, G., Everitt, B.S., Dover Publ., 2004.
    2. Imitation of Life: How Biology is Inspiring Computing, Forbes, N., MIT Press, 2004.
    3. Fuzzy Logic For Beginners, Mukaidono, M., World Scientific, 2002.
    4. Simulation for the Social Scientist, Gilbert, N., Troitzsch, K.G., Open University Press, 2003.
    5. Introducing Game Theory and Its Applications, Mendelson, E.,Chapman&Hall/CRC, 2004.
    6. Games and Decision Making, Aliprantis, C. D., Chakrabarti, S. K., Oxford University Press, 2000.

  • Project: for description, click here.

  • Tentative Schedule:

    WeekTopics in ProblemsTopics in MethodPrjs
    Sep 17
    Introduction, Classification ~Ch 4 Hill climb, Exhaus, Divide&conquer, Brach&bound, Greedy, Dynamic Programming, etc. Finding Topics
    Oct 8
    clustering Neural Networksclassification
    Oct 29
    Optimization/Feature SelectionGeneric Algoritm/ Evolclustering
    Nov 19
    -Hybrid, Fuzzy, Games Optimization/Feature Selection
    Dec 17
    Prj presentation

  • Evaluation:
    • Project presentation (50%):
    • Project report (50%):

  • Course Policies
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