Artificial Intelligence

Aims At the end of this course, the student is able to reason and argue about what type of algorithms and efficient source code to be developed and applied when tackling real-life machine learning tasks; understand the principles underlying effective machine learning methods; use and adapt state-of-the-art machine learning algorithms to tackle a challenge; properly evaluate a machine learning algorithm's performance in a real-life context. Content 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, speech analysis, and many more. The goal of this course is to learn how to tackle specific real-life problems through the selection and application of state-of-the-art machine learning algorithms, notably by entering international machine learning competitions organized at Kaggle.