This exercise represents a slight detour of the previous lecture content from RL into general supervised learning. Basics of applied ML are picked up on, from data wrangling, loading and manipulation to feature engineering and model fitting.
- Data analysis
- Feature engineering
- Cross-validation
- Regression
- Linear models and neural networks
- Classification
- Lienar models and neural networks
Extensive use of the Pandas and scikit-learn toolbox is made.