In this course, you will learn the basic statistical and algorithmic concepts in Machine Learning with a focus on their practical applications in the field of bioinformatics. You will have the opportunity to work on practical problems and implement and use the methods learned during lectures to analyze biological datasets, in particular omics data. For this, you should have experience in programming languages, such as R, Python, Java or C/C++.
We will cover pre-processing of biological data, model implementations and analysis methods. You will learn about models for regression, clustering and classification, feature selection and advanced data preprocessing, such as imputation. We will also cover Deep Learning approaches.
Throughout the course, you will complete weekly exercises and present your results to the class. These exercises are designed to reinforce the practical applications of the material covered in lectures.
By the end of the course, you will be able to process data, choose appropriate models to answer specific questions, evaluate results, and effectively communicate your findings through written reports.