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--- courses:be4m33ssu [2017/06/18 13:51]
kasnezde [Zkouška]
+++ courses:be4m33ssu [2025/01/03 18:23] (aktuální)
@@ Řádek 9: / Řádek 9: @@
 ===== Zkouška =====
 ==== 27.1.2017 ====
@@ Řádek 15: / Řádek 16: @@
 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/

courses/be4m33ssu.1497786682.txt.gz · Poslední úprava: 2025/01/03 18:16 (upraveno mimo DokuWiki)

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