Holiday
EECS資電 207 T4R3R4
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, multilla<x>yer 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 (neuronal dynamics, spiking neuron network and Hopfield model) 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. 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 (20%) and Midterm and final exam (80%) 採用下列何項 AI 使用規則 (Indicate which of the following options you use to manage student use of the AI) 禁止使用,請註明相關的監管機制 Prohibited use; please specify relevant oversight there will be some random checking of usage of AI tool such as ChapGPT. Our course is the core of AI course so basically, all the homework design will be aimed to to outsmart the commercial AI tool.
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Average Percentage 88
Std. Deviation 10.26
平均百分制 84.53
標準差 10.16
平均百分制 86.82
標準差 12.56
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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