Machine Learning Seminar for Rookies

Textbook: Tom Mitchell, Machine Learning, McGraw Hill, 1998

Room: 301-412

Time: 14:00 ~ 17:00

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Date

Chapter

Speaker

Supervisor

01/22/08

    1. Introduction

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    2. Concept Learning

¹ÚÂùÈÆ

01/24/08

    3. Decision Tree Learning

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    4. Artificial Neural Networks

½Å¿øÁø

01/29/08

    5. Evaluating Hypotheses

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    6. Bayesian Learning

ÀÌ»óÀ±

01/31/08

    7. Computational Learning Theory

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    8. Instance-Based Learning

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02/12/08

    9. Genetic Algorithms

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    10. Learning Set of Rules

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02/14/08

    11. Analytical Learning

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¾öÀçÈ«

    12. Combining Inductive and Analytical Learning

À̼º¹è

02/19/08

    13. Reinforcement Learning

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À̽ÂÁØ

  14. Generative and Discriminative Classifiers:
      Naive Bayes and Logistic Regression

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02/21/08

   Introduction to Bioinformatics and ML

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   Introduction to Neuroscience and ML

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    Introduction to Cognitive science and ML

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02/28/08

    Introduction to weka

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http://bi.snu.ac.kr/SEMINAR/ML/¿¡ ³õ¿©ÀÖ´Â ±âÁ¸ ML ¼¼¹Ì³ª ÀڷḦ ½áµµ ÁÁ°Ú½À´Ï´Ù.


14 Àå ¿øº»ÀÇ ¸µÅ©´Â http://www.cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf ÀÔ´Ï´Ù.