====== Statistické strojové učení ======

  * Stránky předmětu: [[https://cw.fel.cvut.cz/wiki/courses/BE4M33SSU/start|Statistical Machine Learning]]
  * Přednášející a cvičící: [[http://cmp.felk.cvut.cz/~flachbor/|Boris Flach]], [[http://cmp.felk.cvut.cz/~xfrancv//|Vojtech Franc]], [[http://cs.felk.cvut.cz/en/people/drchajan|Jan Drchal]]

===== Cvičení =====



===== Zkouška =====


==== 27.1.2017 ====
{{:courses:courses_be4m33ssu_20170127.jpg?direct&200|}}

FIXME doplňte prosím zbytek příkladů
~~DISCUSSION~~


===== Helpful materials =====

**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**

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/