Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course

Learning outcomes and overarching aims of this course

The course introduces a variety of central algorithms and methods essential for studies of data analysis and machine learning. The course is project based and through the various projects, normally three, you will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. The students will learn to develop and structure large codes for studying these systems, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course.

Understand linear methods for regression and classification;
Learn about neural network;
Learn about bagging, boosting and trees
Support vector machines, not covered
Learn about basic data analysis;
Be capable of extending the acquired knowledge to other systems and cases;
Have an understanding of central algorithms used in data analysis and machine learning;
Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++.