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Lecturer(s)
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Fürst Tomáš, RNDr. Ph.D.
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Hron Karel, prof. RNDr. Ph.D., DSc.
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Pavlačka Ondřej, RNDr. Ph.D.
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Course content
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The seminar will deal with current trends in bayesian inference, machine learning, applied mathematics, Data Science, etc. The aim of the seminar is to provide a platfomr for the communication among students, academic staff and professionals from outside Academia
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Learning activities and teaching methods
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Monologic Lecture(Interpretation, Training), Dialogic Lecture (Discussion, Dialog, Brainstorming)
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Learning outcomes
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To follow what is currently happening in the field of Data Science, if possible outside academia
Orientation in the world of Data Science
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Prerequisites
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Interest in the field of Data Science
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Assessment methods and criteria
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Student performance
Active participation at the seminar
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Recommended literature
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Bishop, Ch., Bishop, H. (2024). Deep Learning: Foundations and Concepts.
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Bishop, Ch. (2011). Pattern recognition and machine learning.
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Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep Learning (Adaptive Computation and Machine Learning series).
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Hastie, T., Tibshirani, R. (2016). The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
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MacKay, D. (2003). Information theory, Inference, and learning algorithms.
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Rasmussen, C. E. (2006). Gaussian Processes for Machine Learning.
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