Applied Statistics - Week 6

Tuesday the 2nd - Friday the 5th of January 2018

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:

Lady tasting tea (Wikipedia).

Short note on Lady tasting tea.


Tuesday:
We will use two days for the last "major" theme in these course, which is MultiVariate Analysis (MVA), that is analysis of data with more than one (typically many) variables. To begin with, we will consider the relatively simple linear case, which is described by Fisher's Discriminant, but Friday move on to more complex sets of data, for which more advanced non-linear methods, such as Neural Networks (NN) and Boosted Decision Trees (BDT) are more useful.

Reading:

NOTE: You should by now have read curriculum (roughly Barlow chapters 1-8).
Lecture(s):

AS2017_0102_MultiVariateAnalysis1.pdf
Computer Exercise(s):

2par_discriminant.py

fisher_discriminant.py and data

Friday:
Reading:

Lecture(s):

AS2017_0105_MultiVariateAnalysis2.pdf
Computer Exercise(s):

MachineLearningExample.py and
associated data sample: DataSet_ML.txt.

Example of Decision Tree.

Finally, a link to an online course on Machine Learning (by Udacity), which I was recommended.

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Last updated: 1st of January 2018.