Machine Learning - Examination number: 9958243S

Machine Learning studies concepts and algorithms which enable computers to detect patterns and structures automatically.

Table of contents

1. Introduction
2. Concepts of machine learning
   
   • Methods of machine learning
   • Training data and test data
   • Merit function and learning rate
   • Examples
3. Basic concepts
   
   • Vector, matrix, norm
   • Introduction to Python
   • Mathematical optimization
   • Statistical concepts
4. Introduction to neural networks
   
   • Andrei Kolmogorov and Hilbert’s 13th problem
   • Concepts and algorithms
   • Examples
5. Supervised learning
   
   • Regression problems
   • Classification problems
   • Support Vector Machines (SVM)
   • Examples
6. Unsupervised learning
   
   • Cluster analysis
   • Principal component analysis (PCA)
   • Examples

Ein deutschsprachiges Skript zur Vorlesung ist vorhanden.

References
[1] C. M. Bishop, Pattern recognition and machine learning, New York: Springer, 2006.
[2] T. Hastie, R. Tibshirani, J. Friedman, The elements of statistical learning, New York: Springer, 2017.
[3] S. Russell, P. Norvig, Artificial Intelligence, Boston: Pearson, 2016.

Lecturer: Bittner
Last update: 10.08.2023