Although many tedious tasks can be automated by modelling the behavior of a (computer) system manually, many problems require that a system can adapt its reponses based on feedback on former actions, i.e., learn how to act in a better way in the future. Other tasks are just too large-scale for humans to overview, so help from computers is needed.
Machine Learning (also known as Data Mining, Pattern Recognition, Data Analysis, and Classification) is a research area at the intersection of computer science, artificial intelligence, mathematics and statistics, that addresses these problems. It covers general methods and techniques that then can be applied to a vast set of applications such as predicting customer behavior, steering a robot, detect spam, and predict the folding of a protein, to name just a few.
In this lecture, we will study the most basic learning problems, starting with regression and classification problems (supervised learning). Here, we will look at different models such as linear classifiers, decision trees, neural networks, support vector machines, and simple types of Bayesian networks. Later on we also will look into clustering and dimensionality reduction (unsupervised learning).
The Machine Learning course is prerequisite for most advanced courses and seminars of ISMLL. It is recommended for students from third term onwards.
Textbooks:
- Christopher M. Bishop (2006): Pattern Recognition and Machine Learning.
- Trevor Hastie, Robert Tibshirani, Jerome Friedman (2001): The Elements of Statistical Learning.
- Richard O. Duda, Peter E. Hart, David G. Stork (2001): Pattern Classification, 2nd edition.
Lecturer: Dr. Steffen Rendle
Tutorial: Christoph Freudenthaler
