#### Overview of this book

The F# functional programming language enables developers to write simple code to solve complex problems. With F#, developers create consistent and predictable programs that are easier to test and reuse, simpler to parallelize, and are less prone to bugs. If you want to learn how to use F# to build machine learning systems, then this is the book you want. Starting with an introduction to the several categories on machine learning, you will quickly learn to implement time-tested, supervised learning algorithms. You will gradually move on to solving problems on predicting housing pricing using Regression Analysis. You will then learn to use Accord.NET to implement SVM techniques and clustering. You will also learn to build a recommender system for your e-commerce site from scratch. Finally, you will dive into advanced topics such as implementing neural network algorithms while performing sentiment analysis on your data.
F# for Machine Learning Essentials
Credits
Foreword
Acknowledgments
www.PacktPub.com
Preface
Free Chapter
Introduction to Machine Learning
Information Retrieval
Collaborative Filtering
Sentiment Analysis
Index

## Strategy to convert a collective anomaly to a point anomaly problem

A collective anomaly can be converted to a point anomaly problem and then solved using the techniques mentioned above. Each contextual anomaly can be represented as a point anomaly in N dimension where N is the size of the sliding window. Let's say that we have the following numbers: `1;45;1;3;54;1;45;24;5;23;5;5`. Then a sliding window of size 4 will produce the following series of collections can be generated by the following code

This produces the following lists:

```val data : int list = [1; 45; 1; 3; 54; 1; 45; 24; 5; 23; 5; 5]
val windowSize : int = 3
val indices : int list list =
[[1; 45; 1]; [45; 1; 3]; [1; 3; 54]; [3; 54; 1]; [54; 1; 45];
[1; 45; 24];[45; 24; 5]; [24; 5; 23]; [5; 23; 5]; [23; 5; 5]]
```

Now, as you have seen before, all of these lists can be represented as one point in three dimensions and Grubb's test for multivariate data.