Presentation: NET Machine Learning: F# and Accord.NET
- Understand how to start applying machine learning in real-world projects with .NET
- See practical examples leveraging F# and Accord.NET
- Learn several machine learning algorithms (covering regression, classification and clustering) and their use cases
Machine learning is gaining momentum with the increase of necessity to understand data much more efficiently, to predict better for competitive profit and research. In this talk we'll run over various machine learning algorithms available in the Accord.NET - a framework for machine learning and scientific computing in .NET. We'll also have a look at sample types of problems to see how we can apply machine learning algorithms using Accord.NET framework with F# functional approach.
Interview with Alena Hall
QCon: Can you tell me about functional programming with .NET?
Alena: I really like functional programming, and I think there should be more functional programmers. When it comes to .NET, we have F# (a multi-paradigm open source language). We can do both object oriented and functional programming, and it has full interoperability with C#. It also runs on Windows, Linux, and OSX. You can write applications on Windows Phones, iPhone, and Android. All of F# is transparent and absolutely open on github.
QCon: What is Accord.NET?
Alena: Accord.net is a machine learning framework. It is written in C# and was originally implemented to work with machine learning algorithms and for audio/video processing.
You can find algorithms for supervised and unsupervised learning, containing Logistic Regression, Support Vector Machine, different kernels, statistics and lots of other algorithms. It is a tool set that you can use as a software engineer if you want to use already implemented machine learning algorithms.
QCon: How do you plan to go about discussing Machine Learning, Accord.NET, and F#?
Alena: In my talk, I’m going to first talk about machine learning in general. What is machine learning? Why are some of the use cases? Then, how software engineers can apply machine learning in their real projects using C# and F#. But I will be mostly focusing on F#, because it really simplifies working with data and makes our code cleaner and easier.
We can use Accord.NET from F# very easily. All the data preparation and preprocessing is really convenient to do with F#, so a lot of the examples will be for using F# with an import of Accord.NET library.
QCon: Most of the engineers at QCon are very senior. Does your talk target senior level engineers?
Alena: The level of my talk is for any kind of software engineer. I’m not sure what the the level of machine learning is across the conference, but it is good that most of the people will be senior. The audience should have basic mathematics and basic language concepts. I will mostly be focusing on machine learning algorithms and not on basic language constructs.
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