C.L.Liu炯朗206 W3W4F3F4
Probability is arguably one of the most important math tool in the areas such as computer science, information theory, machine learning, and artificial intelligence. The course will be taught with a clear goal of using probability as a computational tools with many examples from practical applications. Fundamental aspect such as axioms, conditional probability, random variables, and joint distribution will be covered. Efforts will be made to make sure that the students learn the important foundational concepts necessary for their subsequent exploration of modern machine learning and artificial neural network.
Course keywords: probability, statistics, random variable, information theory, machine learning, inference Course Description Probability is arguably one of the most important math tool in the areas such as computer science, information theory, machine learning, and artificial intelligence. The course will be taught with a clear goal of using probability as a computational tools with many examples from practical applications. Fundamental aspect such as axioms, conditional probability, random variables, and joint distribution will be covered. Efforts will be made to make sure that the students learn the important foundational concept necessary for their subsequent exploration of modern machine learning and artificial neural network. Textbook Saeed Ghahramani, Fundamentals of Probability with Stochastic Process, 3rd ed. Pearson. References: 1.David J. C. Mackay, Information theory, inference, and learning algorithm, Cambridge University Press, 2003. 2. Ethem Alpaydın, Introduction to machine learning, 2nd edition, MIT Press. 3. David Applebaum, Probability and information, an integrated approach, 2nd edition, Cambridge University Press. teaching Method: oral lecture Syllabus 1. Axioms of Probability 2. Combinatorial methods 3. Conditional Probability and Indepedence 4. Discrete and continuous random variable, Poisson process and survival analysis 5. special topics : entropy and information theory, maximal entropy principle and softmax 6. Covariance, Bivariate normal Distribution and multivariate distribution 7. More on expectation and variance 8. Markov inequality and central limit theorem 9. Selected topics in Markov chain, information theory and machine learning Evaluation: Grading scheme are based on homework and exams. Homework (20%) and midterm (40%) and final exam (40%) AI rules(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) Prohibited use for AI. please specify relevant oversight on AI: We will randomly check the possible usage of generative AI and homework are designed to outsmart the commercial AI tools
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二下必修。每週上課150分鐘,其餘時間由教授彈性運用。
電資院,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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