• The students know the statistical and mathematical foundations of ML methods, the approaches to ML and the basic concepts and techniques.
• Additionally, the students know the importance and the power of feature engineering and the crucial step of model evaluation.
• Further, they are able to apply the ML cycle, incl. handling of real data, data preparation and the application of the learned ML techniques with the scripting languages R and Python.

None

The module covers the following topics/contents:
Foundations of ML:

• Mathematics for ML, incl. matrix decompositions, calculus, gradients
• Overview on discrete and continuous probability distributions
• Basics of statistical learning, incl. loss function, decision analysis, Bayes decision, graphical models and model selection
• Basics of multivariate statistics
• Overview on optimization methods (Gradient Descent, Stochastic gradient descent, Constrained and Convex Optimization)

• Introduction to primary approaches to machine learning
• Machine learning concepts and techniques
• Anatomy of a learning algorithm & overview of fundamental algorithms (Regression, Support Vector Machine, Clustering, ...)
• Filtering techniques
• Feature engineering
• Model inference and prediction
• Model evaluation (Confusion matrix, Accuracy, F1-score, Precision, Recall, Cross validation, ...)

• Introduction to script languages (R/Python)
• The machine learning cycle (data analysis pipeline)
• Data preparation techniques (outlier detection, missing values, data structures, error and noise...)

• Lecture script as provided in the module (required)
• S. Guido: Introduction to Machine Learning with Python: A Guide for Data Scientists. O'Reilly UK Ltd, 2016
• S. Shalev-Shwartz and S. Ben-David: Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014
• D. Conway and J.M. White: Machine Learning for Hackers. O'Reilly UK Ltd, 2012
• T. Hastie, R. Tibshirani and J. Friedman: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2009

Integrated course - teaching & discussion, guest lectures by specialists, demonstration, exercises and practical examples in the lab, home work

immanent examination character: presentation, assignment reports, written/oral exam