CII504 Intelligent Engine © 2005 Irfan Subakti Department of Informatics Institute Technology of Sepuluh Nopember Surabaya - Indonesia

Lecture 1© 2005 Irfan Subakti2 Outline Course overview

Lecture 1© 2005 Irfan Subakti3 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.

Lecture 1© 2005 Irfan Subakti4 Course overview (cont.) 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, Na ï ve 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

Lecture 1© 2005 Irfan Subakti5 Course overview (cont.) References Tom M. Mitchell, Machine Learning, International Edition, McGraw- Hill, Singapore, Mitsuo Gen, Runwei Chen, Genetic Algorithms and Engineering Design, John Wiley & Sons, Inc., New York, USA, Richard O. Duda, Peter E. Hart, David G. Stork, Pattern Classification, Second Edition, John Wiley & Sons, Inc., USA, Stuart J. Russell and Peter Norvig, Artificial Intelligence – A Modern Approach, Second Edition, Prentice Hall – Pearson Education, Inc., New Jersey, USA, IEEE Transactions on Neural Networks, The Institute of Electrical and Electronics Engineers, Inc. IEEE Transactions on Pattern Analysis and Machine Intelligence, The Institute of Electrical and Electronics Engineers, Inc.