Statistické strojové učení

• Stránky předmětu: Statistical Machine Learning
• Přednášející a cvičící: Boris Flach, Vojtech Franc, Jan Drchal

Literature https://web.stanford.edu/~hastie/Papers/ESLII.pdf Majority of SSU subjects understandably explained here: http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf

SVM MIT best :) https://www.youtube.com/watch?v=_PwhiWxHK8o MIT explains, what is SVM, how it works, how we derive dual optimization equation, fundamental components of SVM. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/tutorials/MIT6_034F10_tutor05.pdf

https://www.youtube.com/watch?v=IOetFPgsMUc + pokracovanie v part II. a III.

Neural nets + convolutional https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv Whole course on neural nets and convolutional networks. Very comprehensive lectures, explained from the basic concepts plus nice motivation examples.

MLE First what is likely hood? VYD lecture slides (course on FEL,CTU) https://www.youtube.com/watch?v=2vh98ful3_M EM + gaussian mixture MIT notes for same topic. But it is useful to understand MLE first. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/lecture-notes/lec15.pdf

Bayes learning MIT lecture notes once agian https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/tutorials/MIT6_034F10_tutor06.pdf

GMB http://blog.kaggle.com/2017/01/23/a-kaggle-master-explains-gradient-boosting/

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