Statistické strojové učení

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

doplňte prosím zbytek příkladů

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 Lecture on SVM on MIT https://www.youtube.com/watch?v=_PwhiWxHK8o MIT notes: 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 notes: 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/