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CI1504 - Intelligent Engine

Second Semester 2005/2006

References

1. Tom M. Mitchell, Machine Learning , International Edition, McGraw-Hill, Singapore, 1997.
2. Mitsuo Gen, Runwei Chen, Genetic Algorithms and Engineering Design , John Wiley & Sons, Inc., New York, USA, 1997.
3. Richard O. Duda, Peter E. Hart, David G. Stork, Pattern Classification , Second Edition, John Wiley & Sons, Inc., USA, 2001.
4. Stuart J. Russell and Peter Norvig, Artificial Intelligence – A Modern Approach , Second Edition, Prentice Hall – Pearson Education, Inc., New Jersey, USA, 2003.
5. IEEE Transactions on Neural Networks , The Institute of Electrical and Electronics Engineers, Inc
6. IEEE Transactions on Pattern Analysis and Machine Intelligence, The Institute of Electrical and Electronics Engineers, Inc.

Module

• Intelligent Engine

Slides

1. Introduction
2. Knowledge-based Systems
3. Ripple Down Rules (RDR)
4. Single Classification RDR
5. Multiple Classification RDR
6. RDR Application: EMMA (An E-Mail Management Assistant)
7. Fuzzy Concept in Agen Technology: Speedy Agent Car
8. Fuzzy Concepts in Expert Systems
9. GA-Fuzzy Application in RDBMS
10. SA, GA and GSA in Fuzzy Systems
11. GSA-Fuzzy Application in RDBMS
12. Multiple Null Values in GSA-Fuzzy applied in RDBMS
13. A Variable-Centered Intelligent Rules System (VCIRS)
14. Proceeding of ICTS 2005, 11th August 2005

From Machine Learning, by Tom M. Mitchell, International Edition, McGraw-Hill, Singapore, 1997.

1. Mitchell, T.M., Machine Learning, Chapter 1: Introduction
2. Mitchell, T.M., Machine Learning, Chapter 2: Concept Learning and the Genreral-to-Specific Ordering
3. Mitchell, T.M., Machine Learning, Chapter 3: Decision Tree Learning
4. Mitchell, T.M., Machine Learning, Chapter 4: Artificial Neural Networks
5. Mitchell, T.M., Machine Learning, Chapter 5: Evaluating Hypotheses
6. Mitchell, T.M., Machine Learning, Chapter 6: Bayesian Learning
7. Mitchell, T.M., Machine Learning, Chapter 7: Computational Learning Theory
8. Mitchell, T.M., Machine Learning, Chapter 8: Instance-Based Learning
9. Mitchell, T.M., Machine Learning, Chapter 9: Genetic Algorithms
10. Mitchell, T.M., Machine Learning, Chapter 10: Learning Sets of Rules
11. Mitchell, T.M., Machine Learning, Chapter 11: Analytical Learning
12. Mitchell, T.M., Machine Learning, Chapter 12: Combining Inductive and Analytical Learning
13. Mitchell, T.M., Machine Learning, Chapter 13: Reinforcement Learning

News

Score

• Intelligent Engine Score

Course overview

• Credit
  • 3 Credits (50 minutes x 3 = 150 minutes)

• Prerequisites
  • Artificial Intelligence (CI1420)

• Goals
  • Student are able to get the understanding about machine learning which has relation with the computer program. It can improve its performance by training or learning set
  • Student will get knowledge theoretic about several concepts: inductive bias, Probability Approximately Correct (PAC) and the Mistake bound learning frameworks, Minimum Description Length principle and Occam ' s Razor.
  • Student will get practical applications , i.e., learning method algorithms such as: Decision Trees learning, Neural Network learning, Statistical Learning methods, Genetic Algorithms, Bayesian Learning methods, Explanation-based learning and Reinforcement learning.

• Contents
  • Introduction to machine learning
    • Well-posed learning problems, designing a learning system: choosing the training experience, choosing the target function, choosing a representation for the target function, choosing a function approximation algorithm, final design; perspectives and issues in machine learning
  • Concept learning
    • Concept learning task: notation and the inductive learning hypothesis, concept learning as search at space hypotheses, version spaces, inductive bias.
  • Decision Tree learning
    • Decision tree representation, the basic decision tree learning algorithm, hypothesis space search in decision tree learning, inductive bias in decision tree learning, issues in decision tree learning: overfitting, incorporating continuous-valued attributes, handling training examples with missing attribute values
  • Artificial Neural Networks
    • Neural network representations, perceptrons, multilayer networks and the Backpropagation algorithm, problems with: convergence and local minima, hidden layer representations, generalization, overfitting, and stopping criterion
  • Bayesian Learning
    • Bayes theorem and concept learning: Brute-force Bayes concept learning and MAP hypotheses and consistent learners, Maximum likelihood and Least-squared error hypotheses, Minimum description length principle, Bayes optimal classifier, Naïve Bayes classifier, Bayesian belief networks, the Expectation Maximization (EM) algorithm
  • Genetic Algorithm
    • Representing hypotheses, genetic operators, fitness function and selection, hypothesis space search
  • Reinforcement Learning
    • Q learning: the Q function, an algorithm for learning Q, Convergence; nondeterministic rewards and actions, temporal difference learning

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