Applied Statistics - Week 7

Monday the 7th - Friday the 11th of January 2019

ERDA shared link to full week material: cgccp2cJPx

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:
  • Some Machine Learning links will most likely appear here!

    Monday:
    While Fisher's Discriminant is a powerful (and transparent) tool, it is superseded by the more performant Machine Learning algorithms, which this lecture and exercise is meant to wet your appetite for.

    Reading:
  • No formal reading.
    Lecture(s):
  • AS2018_01_07_MultiVariateAnalysis2.pdf
    Computer Exercise(s):
  • MachineLearningExample.py and
    associated data sample: DataSet_ML.txt.
  • Illustration of Decision Tree through an interactive example.

    Tuesday:
    We will spend both Tuesday and Friday on a larger exercise, which illustrates the idea of separating data into catagories, and how to measure and optimise the performance of this. 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.
    The subject is slightly associated with Bayes' Theorem, as there are not an equal amount of each type of particle in the beam. Many of you know this theorem already, but with this exercise I'll try to bring a general perspective on data analysis along with it.

    Reading:
  • Barlow, chapter 7.
  • Possibly (review of) Bayes' Theorem
    Lecture(s):
  • Test beam data introduction
  • I will give an introduction to todays exercise on ATLAS testbeam data.
    Computer Exercise(s):
  • Analysis of ATLAS testbeam data: ATLAStestbeam.ipynb and data.


    Friday:
    Reading:
  • No reading - focus on ATLAS test beam data analysis.
    Lecture(s):
  • I will go through the problem set and its solution.
  • Problem Set - Solutions and Comments
    Computer Exercise(s):
  • We will continue with the ATLAS testbeam exercise, getting some key figures.
  • Analysis of ATLAS testbeam data: ATLAStestbeam.ipynb and data.


    Finally, a link to an online course on Machine Learning (by Udacity), which I was recommended.
    Last updated: 3rd of January 2019.