Applied Statistics - Week 6

Tuesday the 2nd - Friday the 5th of January 2024

The following is a description of what we will go through during this week of the course. The chapter references and computer exercises are considered read, understood, and solved by the beginning of the following class, where I'll shortly go through the exercise solution.

General notes, links, and comments:
  • A neat tree-based algorithm is XGBoost, described in the nice XGBoost paper.
  • An alternative which is faster and roughly equally performant is LightGBM.

    Tuesday:
    We will start the new year with an introduction to Machine Learning, which are non-linear methods for classification and regression, typically based on algorithms such as Boosted Decision Trees (BDT) and Neural Networks (NN).
    While Fisher's Discriminant is a powerful (and transparent) tool, it is superseded by the more performant Machine Learning (ML) algorithms, which this lecture and exercise is meant to whet your appetite for. Note that Machine Learning is not part of the curriculum.

    Reading:
  • No formal reading, but please consider these introductions to Decision Trees and Neural Nets.
  • Further inspiration can be found here: ML links.
    Lecture(s):
  • Introduction to Machine Learning
  • Recording of Lecture video.
    Computer Exercise(s):
  • MachineLearningExample.ipynb and associated data sample: DataSet_ML.txt.

    Finally, a link to an online course on Machine Learning (by Udacity).
    Alternatively, the NBI Applied Machine Learning course runs in block 4 (Schedule C).

    Friday:
    We will spend both Monday and Tuesday on a larger exercise, which illustrates the idea of separating data into catagories, and how to measure and optimise the performance of this in real data with all of its quirks and twists. The data is from ATLAS testbeam data at CERN and deals with separating particles in a beam into electrons and pions, but could in principle be from any other area of research and/or business.

    Reading:
  • No reading - focus on ATLAS test beam data analysis.
    Lecture(s):
  • Stratification - beating the sqrt(N) law!
  • Real data analysis - ATLAS testbeam
  • Recording of Lecture video.
    Computer Exercise(s):
  • The exercise is on the real ATLAS testbeam data (PDFs unknown!), where the use of three independent detectors is key.
  • Analysis of ATLAS testbeam data: ATLAStestbeam.ipynb along with main data (2 GeV) and alternative data (9 GeV).
    Last updated: 29th of December 2023.