GEN IV綜四121 T5T6R6
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. This course will cover topics, Bayesian decision theory, parametric methods, multivariate method, multil<x>ayer perceptron, and reinforcement learning.
Course keywords: machine learning; statistical learning theory; artificial intelligence; neural network Course Description: Machine learning is programming computers to optimize a performance criterion using example data or past experience. We need learning in cases where we cannot directly write a computer program to solve a given problem. This course discusses may methods that have their bases in different fields: statistics, pattern recognition, neural network, artificial intelligence, signal processing, control, and data mining. Examples from wide variety of subject such as biology, control, statistical mechanics and robotics will be given as proper context for machine learning. Programming codes as embodiment of the algorithm in machine learning will be analyzed in details. Syllabus 1. Introduction; probability theory and statistics; things you need for computational thinking; random number generator and Monte Carlo method 2. Supervised learning 3. Baysian decision theory 4. Parametric method 5. Multivariate method 6. Clustering 7. non-parametric method 8. linear discriminant 9. multi-layer perceptron; neural network 10. Deep learning 11. Reinforcement learning 12. Special topics on current research frontier (spiking neuron network) Textbook 1. Ethem Alpaydin, Introduction to machine learning, 4th edition, MIT Press. References: 1. Artificial Intelligence, Stuart Russel and Peter Norvig, Prentice Hall.1995 2. Masashi Sugiyama, Statistical Reinforcement Learning CRC 3. Duda, R.O. P. E. Hart, and D. G. Stork 2001 Pattern Classification, 2nd ed. New York: Wiley. (Excellent book on neural network. Plenty of good figures to study.) Method: Powerpoint sides will be used for teaching. Grading& Evaluation Homework (40%) and Midterm and final exam (60%)
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本學期增開課程, 非常態開設。
電機系大學部3年級4年級,電資院學士班大學部3年級4年級優先,第3次選課起開放全校修習
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