At the end of this course, the student is able to
- reason and argue about what type of algorithm to apply to what type of machine learning problem;
- understand the basic principles underlying some popular machine learning algorithms;
- implement a state-of-the-art machine learning algorithm;
- properly evaluate a machine learning algorithm's performance.
Machine learning addresses the fundamental problem of developing computer algorithms that can harness the vast amounts of digital data available in the 21st century and then use this data in an intelligent way to solve a variety of real-world problems. Examples of such problems are recommender systems, (neuro)image analysis, intrusion detection, spam filtering, automated reasoning, systems biology, medical diagnosis, microarray genomics, and many more. The goal of this course is to highlight some of the state-of-the-art machine learning algorithms and to apply them to actual problems, by entering one of the major running machine learning competitions.