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)
Prerequisites
- Basic linear algebra
- Basic analysis
- Basic capability of python
Contents
- 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
References
- 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)