Tv show Numb3rs using statistics

Getting students engaged on a highly quantitative subject such as statistics or analytics is not an easy task.
Here I would like to share a TV series named Numb3rs which has been very popular and has attracted masses. I myself have been a huge fan of this show.
It’s about two brothers, the older one an FBI agent who constantly consults his younger brother, a math professor, in solving crimes. Each crime is solved using different mathematical analysis tools be it on profiling criminals, predicting their movements, identifying locations of victims, or some IT stuff such as image enhancement of surveillance videos or hacking computer systems.

Numb3rs opening line for every episode:
We all use math every day; to predict weather, to tell time, to handle money. Math is more than formulas or equations; it's logic, it's rationality, it's using your mind to solve the biggest mysteries we know.
One of the episodes in season 2 makes use of Linear discriminant analysis

Linear discriminant analysis (LDA), is sometimes referred to as Fisher's linear discriminant (although Fisher's original article The Use of Multiple Measures in Taxonomic Problems (1936) actually describes a slightly different discriminant, which does not make some of the assumptions of LDA such as normally distributed classes or equal class covariances). LDA is typically used as a feature extraction step before classification.

The episode vividly portrays the intricacies of face recognition using discriminant analysis. Despite the availability of commercial systems, face recognition continues to be an active topic in computer vision research. Current face recognition systems perform well under nearly ideal circumstances, but tend to suffer when variations in expression, illumination, decoration (i.e.,
glasses, facial hair), and/or pose are present. Most current face recognition research aims to improve recognition performance in the presence of such confounding factors. Face
recognition methods can be classified broadly into two : feature- or template-based.
a modular linear discriminant analysis (LDA) approach for face recognition. A set of observers is trained independently on different regions of frontal faces and each observer projects face images to a lower-dimensional subspace.
These lower-dimensional subspaces are computed using LDA methods, including a new algorithm that we refer to as direct,weighted LDA or DW-LDA. DW-LDA combines the advantages of two recent LDA enhancements, namely direct LDA (DLDA) and weighted pairwise Fisher criteria. Each observer performs recognition independently and the results are combined
using a simple sum-rule. Experiments compare the proposedapproach to other face recognition methods that employ linear dimensionality reduction. These experiments demonstrate that the modular LDA method performs significantly better than other linear subspace methods. The results also show that D-LDA does not necessarily perform better than the well-known principal component analysis followedby LDA approach.

Statistics plus videos plus numb3rs not a bad option !

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Gayathri
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