Lectures (Part 1, 3 LP): Describing, including examples, what is machine learning and its applications to biopharmaceutical research. Introduction to learning algorithms, with focus on classifiers, and the types of data associated with each algorithm. Representation of input data through features. Methods for pre-processing and selection of best features. Measures to evaluate the performance of the created models. Discussion of examples of machine learning tools for biopharmaceutical applications.

Seminar (Part 2, 2 LP): The students will learn how to use established machine learning tools and how to build their own model using WEKA. WEKA is a platform that provides implementations of the techniques that will be addressed in the course. WEKA permits the application of machine learning techniques via a graphical user interface that allows users that are not knowledgeable in programming to use and develop machine learning models. The students will be given the task of developing and evaluating a simple machine learning model using a given dataset.