Applied Machine Learning - Week 1

Monday the 25th - Friday the 29th of April 2022

Groups: For the final project you should find a group (administrated by Azzurra), but we highly recommend that you also work/collaborate/discuss in a group for exercises.

Monday 25th of April (afternoon):
Lectures: Intro to course, outline, groups, and discussion of data and goals (TP, VR, RS, AD). Introduction to Machine Learning (TP).
Course information can be found here: ML2022_CourseInformation.pdf
     Recording of last year's Lecture (467 MB) (for reference).

Exercise: Setup of infrastructure (Github, ERDA, Zoom, Slack). Test your Python setup with ML_MethodsDemos.ipynb.
     Getting a feel for the Curse of Dimensionality, making life in high dimensions a lonely one!
     Inspecting data and making a "human" decision tree for classification of b-quark jets in Aleph data:
     Code for initial analysis: BjetSelection.ipynb (classifying b-jets using if-sentences!)
     Data: READMEAlephBtagData.txt (2 kB), AlephBtag_MC_small_v1.csv (2.8 MB), and AlephBtag_MC_small_v2.csv (2.8 MB).


Wednesday 27th of April (morning):
Lectures: Introduction to Tree-based algorithms (TP).
     Additional slides: ML2022_Example_HousingPrices.pdf (2.8 MB)
     Recording of last year's Lecture (314 MB).

Exercise: Exercise: Classification of b-quark jets in Aleph data with Tree based methods.
     Compare performance to your own Decision Tree and the Aleph NN.
     Additional (reference) data, on classifying stars, galaxies, and quasars: Data_SDSS.txt (6.3 MB).


Wednesday 27th of April (afternoon):
Lectures: Introduction to NeuralNet-based algorithms (TP).
     Additional slides: ML2022_AppliedML_Top10.pdf (100 kB)
     Recording of last year's Lecture (331 MB).

Exercise: Exercise: Classification of b-quark jets in Aleph data with Neural Net based methods.
     Compare performance to your tree based method(s) and the Aleph NN.
     Challenge: Given a "large" dataset on b-jets (AlephBtag_MC.csv (168 MB)), see how performance improves with data size.



Example solutions from week 1:
The following are example solutions and related code, which comes with absolutely no warrenty. However, you may let yourself be inspired by these solutions:

Solution example using LightGBM (tree based) and MLPClassifier (NN based).

Solution example using Tensor Flow and PyTorch (both NN based).

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Last updated: 27th of of April 2022 by Troels Petersen.