Learn the basics of machine learning, with a special focus on sparse data as they occur in high dimensional ‘omics’ types of data.
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
The student will also be familiar with concepts of evaluating classifiers, such as Cross-validation and Bias-Variance tradeoff and have profound knowledge of the reasons for over-fitting and complete separation with high-dimensional data and will able to apply all of these methods to real data.
