Applied Machine Learning - Week 3

Monday the 8th - Friday the 12th of May 2023

Final project groups: Click here to register your group (don't worry - not final).


Monday 10th of May (afternoon):
Lectures: Hyperparameters, Overtraining, and Early stopping (TP). (For reference: Recording of the 2021 lecture).
     Both slides and associated code can be found on GitHub

Exercise: Try to optimise your algorithms with respect to the HyperParameters of your model/architecture.
     Note that for NNs it is the learning rate in particular, which is important, and that you might want a scheduler!

Wednesday 12th of May (morning):
Lectures: Input feature ranking and Shapley values (TP) and ML method performance overview (TP).

Exercise: Apply the different feature ranking methods to e.g. the Aleph b-jet data, and determine which variables are the important ones (consider first all 9 inputs and then the 6 used).
     For a discussion of feature ranking, you may also want to see this Towards-Data-Science discussion.
     When you feel, that you understand feature ranking and SHAP values, feel free to start/work on the Initial project.

Wednesday 12th of May (afternoon):
Lectures: Final projects kickoff, also introducing Potential Projects (TP). Introduction to clustering and related algorithms (TP).

Exercise: You should start the exercise by ensuring that you either have a group, or find collaborators!
     The exercise is to apply clustering algorithms to data of your choice.
     Once you feel comfortable with clustering, you're welcome to work on the initial and/or final project.

     During these exercises you should aim to settle your final project groups and discuss what data to work on.
     Click here to register your group (don't worry - not final).

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Last updated: 4th of May 2023 by Troels Petersen.