MF#K + CopenhagenR: Machine Learning with F# and R (with 2 × F# MVPs) – Københavns Universitet

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Datalogisk Institut, DIKU > Begivenhedsmappen > Begivenheder 2017 > MF#K + CopenhagenR: Ma...

MF#K + CopenhagenR: Machine Learning with F# and R (with 2 × F# MVPs)

The F#unctional Copenhageners and CopenhagenR Meetup Groups in collaboration with DIKU Business Club, Laboratory for Applied Statistics and PROSA are hosting this meetup with talks by Evelina Gabasova and Mathias Brandewinder.


  • 18:00: Welcome to the event
  • 18:15: Evelina: Short intro to F# for R people
  • 18:30: Evelina: Exploring StackOverflow Data
  • 19:15: Pause
  • 19:30: Mathias: Agile experiments in Machine Learning with F#
  • 20:30: Thanks for tonight

Exploring StackOverflow Data

Speaker: Evelina Gabasova


When you’re stuck while programming - who you gonna call? StackOverflow! It’s an invaluable source of daily help to many. Interestingly, you can also download the entire data dump of StackOverflow and let machine learning loose on the dataset. In the talk I’ll look at what we can learn from the behaviour of developers worldwide. The dataset can give us answers to many questions - where should I move to find people using my favourite technologies? And is my favourite language used just for hobby projects? We’ll look at how to answer these - and in the meanwhile you will also learn about ideas behind some machine learning algorithms that can give us insights into complex data. I will use a combination of functional language F# with statistical computing language R to show how you can easily access and process large-scale data the functional way.


Evelina Gabasova is currently working as a postdoctoral researcher in the MRC Cancer Unit at University of Cambridge, working with Prof. Ashok Venkitaraman on mathematical models of early carcinogenesis in epithelial tissues. She is interested in developing machine learning models for integrative analysis of heterogeneous biomedical data.

She did her PhD in statistical genomics at University of Cambridge in the MRC Biostatistics Unit, supervised by Lorenz Wernisch. Before coming to Cambridge, she studied for a Master's degree in Computational Statistics and Machine Learning at University College London. While at UCL, she did her Master's project on sparse linear regression models for gene regulatory network inference, under the supervision of David Barber. She completed her previous degrees in theoretical computer science at the Faculty of Mathematics and Physics at Charles University in Prague.

Apart from her academic research, she is also an active member of the F# community. She speaks at conferences and meetups about using F# for data science and machine learning. See more information about Evelina here.

Agile experiments in Machine Learning with F#

Speaker: Mathias Brandewinder


Just like traditional applications development, machine learning involves writing code. One aspect where the two differ is the workflow. While software development follows a fairly linear process (design, develop, and deploy a feature), machine learning is a different beast. You work on a single feature, which is never 100% complete. You constantly run experiments, and re-design your model in depth at a rapid pace. Traditional tests are entirely useless. Validating whether you are on the right track takes minutes, if not hours.

In this talk, we will take the example of a Machine Learning competition we recently participated in, the Kaggle Home Depot competition, to illustrate what "doing Machine Learning" looks like. We will explain the challenges we faced, and how we tackled them, setting up a harness to easily create and run experiments, while keeping our sanity. We will also draw comparisons with traditional software development, and highlight how some ideas translate from one context to the other, adapted to different constraints.


He has been writing code professionally for the past 10 years or so, and enjoying most of it. He used to work mostly in C#, until he discovered F# and fell in love with it. His original background has little to do with programming: in a past life, he studied economics, and operations research. He is interested (among many things) in applied math in general, and machine learning and decision theory in particular. He is a Microsoft .NET / F# MVP, a board member of the F# Software Foundation, He wrote a book, “Machine Learning Projects for .NET Developers”, He enjoys Muay Thai, he likes people and Community building, and he has a tendency to get very absorbed in projects of variable usefulness. See more information about Mathias here.