9.00-9.30: Welcome and coffee

9.30-10.15: What is machine learning? 
Illustrative examples and introduction of key terminology

10.20-11.15: Supervised learning I: regression
Introduction to Jupyter notebooks and Python packages (scikit-learn, numpy). 

• The bias-variance trade-off: short computer exercise on overfitting, high bias, regularization and cross-validation.
• Curse of dimensionality: issues encountered in problems with many predictors. How to apply regularization in high-dimensional space to avoid overfitting.
• Brief introduction to regression algorithms such as neural nets, random forests, Ridge regression.

11.15-11.30 Coffee break

11.30-12.10: Supervised learning I: regression continued

12.15-13.00: Supervised learning II: classification

• Examples: supervised classification in industry and research. 
• How does supervised classification differ from regression?
• Jupyter-notebook: classification of meteorological events. 

13.00-14.00: Lunch break

14.00-14.45: Unsupervised learning: pattern recognition, dimension reduction

• Repetition: how does unsupervised learning differ from supervised learning?
• Algorithm examples: k-means clustering and principal component analysis.

14.50-15.30: Dimension reduction and pattern recognition in practice

• Jupyter-notebook I: PCA Varimax to identify global climate modes.
• Jupyter-notebook II (optional): clustering ocean circulation patterns.

15.30-15.45: Coffee break

15.45-17.00: Combining dimension reduction and regression

• Research example: theoretical background on ozone and climate models.
• Jupyter-notebook: predicting stratospheric ozone using regression models and different input features based on dimension reduction approaches.

9.00-9.45: Recap of previous day and reflection on research example exercise

• Key lessons learned.
• Group reflection on approaches that worked or did not work.

9.50-11.00: Machine learning for time series prediction problems

• Why time series data is special.
• How specific machine learning algorithms can exploit time-structure in data.
• Computer exercise: auto- and cross-correlation of climate modes.

11.00-11.15:  Coffee break

11.15-11.30: Introduction to computer practicals
Two challenges: 

1. Weather prediction
2. Predicting ozone concentrations

11.30-13.00: Hackathon session 1
Participants modify code to combine regression and dimension reduction methods. The aim is to improve the predictive skill given data provided for challenges (a), (b) depending on their choices for algorithms, tuning parameters and input features. Participants can work in sub-groups of two. Each group chooses either (a) or (b) first and will work on the other challenge in session 2.

13.00-14.00: Lunch break

14.00-14.30: Hackathon session 1 cont:
Participants return to the computer exercise and finish their first challenge.

14.30-16.20: Hackathon session 2
Teams swap topics (a) and (b).

15.30-15.45: In between coffee break

16.20-16.30: Submission
Each team/participant submits their best solution for challenges (a) and (b). The solution with the best generalization error on an unseen test dataset wins.
Prize= one Amazon voucher per challenge

16.30-16.50: Discussion of results
What difficulties were encountered?
Which parameters, input features appeared to be most important?

16.50-17.00: Awards & closing remarks
Winners are announce