fsharp + ML is a one-day event that gives you a fast-track to Machine Learning and F#.
The program features a morning of talks by world-class experts, showing how to use F# and machine learning to solve practical problems and an afternoon hands-on workshop to gain practical experience with F# and machine learning.
Machine learning (ML) is becoming an extremely important topic and F# is the perfect tool for doing machine learning (not just) on the .NET platform. There is a lot of interesting ML technologies coming from Microsoft, but also a lot of interesting open-source community projects. This event brings both of the communities together.
There will be talks about interesting F# projects in the area of machine learning and data science, hands-on sessions where we'll help you get started with F# and machine learning and a lot of free space to ask questions and interact.
David is the Program Manager for the F# programming language, working with the open-source F# community and the internal users at Microsoft.
David is a graduate of the Jeffrey S. Raikes School of Computer Science & Management at the University of Nebraska-Lincoln where he first used F# for a university project. He did two internships with Microsoft during his studies, working on the Visual Studio team.
Tomas is a long-time F# enthusiast, Microsoft MVP and author of a book Real-World Functional Programming which explains functional programming concepts using C# 3.0 and teaching F# alongside. He leads functional programming and F# courses in London, New York and worldwide.
Tomas is the lead developer of the FsLab project and a number of other F# open-source libraries for data science including Deedle and formerly F# Data. He also contributed to the development of F# as a contractor at Microsoft Research in Cambridge.
Mathias specializes in the design and development of decision systems and forecasting models. He has been developing .NET applications for the past 10 years, for a variety of industries ranging from biotech to retail and global health. He is very passionate about F#, TDD, and Machine Learning.
He is a Microsoft MVP for F#, leads the San Francisco chapter of the Bay.NET user group, and the San Francisco F# user group and speaks regularly at user group meetings around the world.
Mathias is the author of Machine Learning Projects for .NET Developers, which shows you how to build smarter .NET applications that learn from data, using simple algorithms and techniques that can be applied to a wide range of real-world problems.
In the brief introduction, you'll learn about the F# ecosystem. How does the F# Software Foundation, the F# team at Microsoft and the open-source community around the F# language work? What interesting projects are there, both inside Microsoft and in the community? And how to get in touch and join the community?
The ability to take data, understand it, visualize it and extract useful information from it is becoming a hugely important skill. How can you turn all those logs, histories of purchases and trades or open government data, into useful information that help your business make money?
In this talk, we’ll look at doing data science using FsLab, an F# library for data science. The F# language is perfectly suited for this task – type providers integrate external data directly into the language – your language suddenly understands CSV, XML, JSON, REST services and other sources. The interactive development style makes it easy to explore data and test your algorithms as you’re writing them. Rich set of libraries for working with data frames, time series and for visualization gives you all the tools you need. And finally – F# easily integrates with statistical environments like R, giving you access to the industry standard libraries.
For data exploration and rapid prototyping, the productivity of an interactive scripting environment is hard to beat: simply grab data, run code, and iterate based on immediate feedback. However, that story starts to break down when the data you have to process is big, or the computations expensive. Your local machine becomes the bottleneck, and you are left with a slow and unresponsive environment.
In this talk, we will introduce MBrace.net, an open-source and free engine for scalable cloud programming. Using the MBrace programming model, you can keep working in your beloved familiar scripting environment, and easily execute C# or F# code on a cluster of machines on Azure. We will focus primarily on live demos, from provisioning an Azure cluster with Brisk, to analyzing large datasets in a distributed fashion; in particular, we will discuss how this setup is relevant to data science and machine learning.
Thanks to Microsoft Machine Learning community for sponsoring the lunch break!
To a newcomer, both F# and machine learning may sound very complicated. In our workshop, you'll see that this could not be further from the truth. We will look at solving real-world machine learning problem with the functional-first approach in F# and you'll see that this is a perfect match - functional programming makes it easy to express machine learning algorithms in a clear and easy to understand way.
In this workshop, we'll implement a machine learning algorithm for detecting the language of text, using pages downloaded from Wikipedia (using an F# web crawler) as a training data set. Along the way you'll learn about machine learning concepts, the F# language and functional ideas.
There is so much happening around F#, machine learning and Azure and we have no chance of doing talks and workshops on everything in just one day. That's why we have ask the experts session. We'll introduce a number of interesting machine learning and F# projects from both Microsoft, Microsoft Research and the open-source community. The following experts will be around:
In the rest of the day, you'll have a chance to ask questions and interact. If you are interested in showing your project, drop us an email!.
