Applied Machine Learning - Week 1

Monday the 26th - Friday the 30th of April 2021

Groups: Here you can find suggested collaboration groups (administrated by Zoe).

Monday 26th of April (afternoon):
Lectures: Intro to course, outline, groups, and discussion of data and goals (TP, AA, ZA, CJ, VR). Overview of Machine Learning techniques (TP).

Zoom:Link to lecture.
     Recording of Lecture video (467 MB) and Lecture chat (1 kB)).

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).

     An example solution getting 11.1% wrong with simple selection, and 10.5% wrong with a scan of parameters.


Wednesday 28th of April (morning):
Lectures: Introduction to Tree-based algorithms (TP).
     Additional slides: ML2021_Example_HousingPrices.pdf (2.8 MB)

Zoom:Link to lecture.
     Recording in Lecture video (314 MB) and Lecture chat (3 kB).

Exercise: Exercise: Classification of b-quark jets in Aleph data with Tree based methods.
     Compare performance to your own Decision Tree.

Wednesday 28th of April (afternoon):
Lectures: Introduction to NeuralNet-based algorithms (TP).
     Additional slides: ML2021_AppliedML_Top10.pdf (100 kB)

Zoom:Link to lecture.
     Recording in Lecture video (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).


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) (Troels).

Solution example using DecisionTreeClassifier (tree based) (Rasmus).

Solution example using PyTorch (NN based) (Rasmus).

Solution example using Keras Tensorflow (NN based) (Rasmus).

Solution example II using Keras Tensorflow (NN based) (Marcus).

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Last updated: 1st of May 2021 by Troels Petersen.