The goal of this course is to present the dominant concepts of machine
learning methods including some theoretical background. We'll cover
established machine learning techniques such as Decision Trees, Neural
Networks, Bayesian Learning, Instance-based Learning and Evolutionary
Algorithms as well as some statistical techniques to assess and
validate machine learning results.
Machine Learning is the study of how to build computer systems that
learn from experience. It is a very active subfield of Artificial
Intelligence that intersects with statistics, cognitive science,
information theory, and probability theory, among others. Recently,
Machine Learning has gained great importance for the design of search
engines, robots, and sensor systems, and for the processing of large
scientific data sets. Further applications include handwriting or
speech recognition, image classification, medical diagnosis, stock
market analysis, bioinformatics, etc.
The course will be taught in two parts; the first part consists of
with written examination. The second part of the course will have a more
do-it-yourself character (e.g., practical assignment and/or literature
research) and result in a report and/or presentation.
The course will be taught in English.
Exam and assignment with a written report in teams of 5 students
2BA, 2BA-D, 3CS, 2LI, 3IMM, mBio