A cheat sheet is allowed for the exam, as described in course note from 2020-01-13:
You are allowed to prepare & use one A4 page with handwritten notes (one sided).
Example cheat sheet:
Zkoušky z minulých let, které se mi podařilo získat:
Midterm test, který jsme si letos mohli vypracovat nanečisto.
Solutions by bartefil:
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
https://www.youtube.com/watch?v=IOetFPgsMUc + pokracovanie v part II. a III.
Neural nets + convolutional
3Blue1Brown: Neural Networks (YouTube playlist) Nice basic explanation of how neural networks work. Chapters 3 and 4 provide efficient explanations of backpropagation using good visualizations.
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 likelihood?
EM + gaussian mixture
Andrew Ng: Lecture on clustering, mixture of Gaussians, Jensen's inequality, EM algorithm (CS 229, Stanford University): video, lecture notes
Thomas P. Minka: Expectation-Maximization as lower bound maximization (recommended in lecture slides in 2019)
Bayes learning
GBM https://www.gormanalysis.com/blog/gradient-boosting-explained/