14. Convex vs Non-Convex optimization problems? (5pts each)

Name/Type of the model	Convex / Non-Convex optimization problem?
Linear Regression
Logistic Regression
SVM Linear
SVM RBF
Perceptron
Multilayered Perceptron
Radial Basis Function Neural Network

16. Imagine you have two-dimensional dataset with totally non-correlated features. Can you use PCA to reduce dimensionality to 1D? (5pts)

17. In what cases you might acquire better results with the PCA+LinearRegression instead of just directly running LinearRegression on original dataset? (5pts)

18. In what cases you might acquire worser results with the PCA+LinearRegression instead of just directly running LinearRegression on original dataset? (5pts)

19. In what cases non-Linear dimensionality reduction is preferable over the linear methods (like PCA)? (5pts)

20. Name algorithms of dimensionality reduction / feature selection (5pts)

22. When we dont want to have a regularization? (5pts)

23. Main methods share by all supervised models in scikit learn? (5pts)

25. You have a dataset and need to build a classification model on that. You dont know which model will work better, so you need to try different preprocessing teqchniques and model parameters. Questions:

a) If the dataset is huge what model / parameter selection algorithm (sequence of actions) you will be using? Why? (5pts)

b)If dataset is small what model / parameter selection algorithm (sequence of actions) you will be using? Why? (5pts)

27. Client asks us to build a system that would monitor network behavour of the employees within the organization and would signal if there would be strange behaviour detected. What kind of ML-problem it is (5pts)? What algorithm(-s) we can use? (5pts)

28. We have a task to build a chess-playing program. What kind of ML-problem it is?

29. When you know you might have some outliers in the data, what regression loss you will use? (15 pts)

30. When you know that the classificaion dataset is imbalanced what kind of performance measure you will use? (15 pts)