Having built a system in Event-Driven SOA fashion I’ve come to realize that the moniker applied to this style of …Continue reading →

I got interested in the following question lately: given a data set of examples with some continuous-valued features and discrete classes, what’s a good way to reduce the continuous features into a set of discrete values? What makes this question interesting? One very specific reason is that some machine learning algorithms, like Decision Trees, require discrete features. As a result, potentially informative data has to be discarded. For example, consider the Titanic dataset: we know the age of passengers [...]

Last week, we had our first Coding Dojo at SFSharp.org, the San Francisco F# group – and it was great! A few people in the group had mentioned that at that point they were already convinced F# was a great language, and that what they wanted was help getting started writing actual code, so I figured this would be a good format to try out. What I wanted was something fun, something cool people could realistically achieve under 2 hours. I settled for one of the Kaggle introduction problems, a classic of Machi[...]

Suppose you need to write a script that finds n files, all called based on some pattern, say “c:\temp\my_file_x.txt”, where “x” is replaced by a range of numbers [1..30] for instance, reads the content of these files and glues them together. Suppose also that the files are very small, so you can keep them in […]

In our previous post, we began exploring Singular Value Decomposition (SVD) using Math.NET and F#, and showed how this linear algebra technique can be used to “extract” the core information of a dataset and construct a reduced version of the dataset with limited loss of information. Today, we’ll pursue our excursion in Chapter 14 of Machine Learning in Action, and look at how this can be used to build a collaborative recommendation engine. We’ll follow the approach outlined by the book, starting first with[...]