Introduction to Machine Learning and Data Mining

• Describe the major steps involved in data modeling from preparing the data, modeling the data to evaluating and disseminating the results.
• Discuss key machine learning concepts such as feature extraction, cross-validation, generalization and over-fitting, prediction and curse of dimensionality.
• Sketch how the data modeling methods work and describe their assumptions and limitations.
• Match practical problems to standard data modeling problems such as regression, classification, density estimation, clustering and association mining.
• Apply the data modeling framework to a broad range of application domains in medical engineering, bio-informatics, chemistry, electrical engineering and computer science.
• Compute the results of the data modeling framework by use of Matlab, R or Python.
• Use visualization techniques and statistics to evaluate model performance, identify patterns and data issues.
• Combine and modify data modeling tools in order to analyze a data set of their own and disseminate the results of the analysis.

Structured data modelling. Data preprocessing. Feature extraction and dimensionality reduction including principal component analysis. Similarity measures and summary statistics. Visualization and interpretation of models. Overfitting and generalization. Classification (decision trees, nearest neighbor, naive Bayes, neural networks, and ensemble methods.) Linear regression. Clustering (k-means, hierarchical clustering, and mixture models.) Association rules. Density estimation and outlier detection. Applications in a broad range of engineering sciences.

01001/01002/01003/01004/01005/02402/02403/02002/02101/02102/02525/02631/02632/02633/02692, Basic course in linear algebra and calculus, basic knowledge of probability theory or statistics, basic knowledge of either Matlab, Python or R.

The activities alternate between lectures, problem classes and hands-on Matlab, R or Python exercises (the student can freely choose between these programming languages). Exercises are carried out in teams of 2-3 students.

The course is a basic machine learning course relevant for all technical diploma, bachelor, and master programs. The course is designed as a stand-alone course and provides an introduction to basic machine learning, the mathematics behind the methods, and hands-on experience in their use.