DELTA台達217 T5T6R5R6
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, and random variable will be covered. A lot of examples from information theory and machine learning will be given to illuminate the incredible modeling and computation power in probability theory.
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, and random variable will be covered. A lot of examples from information theory and machine learning will be given to illuminate the incredible modeling and computation power in probability theory. Textbook Saeed Ghahramani, Fundamentals of Probability with Stochastic Process, 3rd ed. Pearson. References: David J. C. Mackay, Information theory, inference, and learning algorithm, Cambridge University Press, 2003. teaching Method: oral lecture and some homework are given to ask the student to do python coding for computation Syllabus 1. Axioms of Probability 2. Combinatorial methods 3. Conditional Probability and Indepedence 4. Discrete and continuous random variable 5. Bivariate Distribution and multivariate distribution 6. Stochastic Process, markov chain 7. Monte Carlo Simulation 8. Statistical Inference 9. Special topics in 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) 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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16週課程,每週上課150分鐘,其餘時間由教授彈性運用。
電機系大學部2年級,電資院學士班大學部2年級優先,第3次選課起開放全校修習
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