At the end of the course, the student:

Will be familiar with and has practical experience with the main methods of machine learning:

o    Nearest neighbors

o    Bayes classifiers and discriminant analyses

o    Decision trees, boosting and random forest

o    Regularization methods and SVM

o    Principal component analysis and partial least squares

o    Neural networks and Deep learning

o    Generalized linear regression

o    Survival analysis

o    Repeated measurements and time course analysis

·Is familiar with concepts of evaluating classifiers, such as Cross-validation and Bias-Variance tradeoff has profound knowledge of the reasons for over-fitting and complete separation with high-dimensional data is able to apply all of these methods to real data.