Aspects of Machine Learning

Intructors: H. Chau Nguyen (email) and Stefan Nimmrichter (email)

Time and place: Monday 12 am, ENC-B030.
Exercise: Sofia Denker (email) and Julia Boeyens (email)


  • Basic linear algebra
  • Basic analysis
  • Basic capability of python


  • General aspects of mechine learning
  • Regression models: RIDGE, LASSO, Elastic Net
  • Principal Component Analysis
  • Classification models: Baysian Reasoning, Naive Baysian Models, Logistic Regression, Support Vector Machine, Decision Tree and Random Forest
  • Neural Networks
  • Unsupervised and Semisupervised Learning
  • Further models in machine learning


  • sklearn tutorial
  • Bischop, C. M., Pattern Recognition and Machine Learning (Springer 2007)
  • MacKay, D. J. C., Information Theory, Inference and Learning Algorithms (Cambridge 2003)
  • Hastie, T., and Frideman, J. H. and Tibshirani, R., The Elements of Statistical Learning (Springer 2001)