What we're about

R-Ladies Utrecht welcomes members of all R proficiency levels, whether you're a new or aspiring R user, or an experienced R programmer interested in mentoring, networking & expert upskilling. Our community is designed to develop our members' R skills & knowledge through social, collaborative learning & sharing. Supporting minority identity access to STEM skills & careers, the Free Software Movement, and contributing to the global R community! 

A local chapter of R-Ladies Global, R-Ladies Utrecht exists to promote gender diversity in the R community worldwide. We are pro-actively inclusive of queer, trans, and all minority identities, with additional sensitivity to intersectional identities. Our priority is to provide a safe community space for anyone identifying as a minority gender who is interested in and/or working with R. As a founding principle, there is no cost or charge to participate in any of our R-Ladies communities around the world.

We are part of Global R-Ladies group. You can access our presentations, R scripts, and Projects on our Github account and follow us on twitter to stay up to date about R-Ladies news!

Website: https://www.rladies.org
Twitter: @RLadiesGlobal ( https://twitter.com/RLadiesGlobal )
Github: https://github.com/rladies

PS Community Policies & Code of Conduct:
The leadership, mentoring & teaching roles within this Community are held exclusively by minority genders (majority gender speakers may be allowed/invited as one-off guests in exceptional circumstances at the leadership team's discretion). Due to unexpected demand, we have opened learning participation to all genders, dependent on initial and on-going vetting by the leadership team. However, the stated priority of the R-Ladies communities is the development & support specifically of those identifying as a minority gender, and we, therefore, reserve the right to guard this interest through whatever measures the leadership team deems appropriate. Anyone involved with R-Ladies Utrecht is expected to fully respect each other, the mandate of this community, and the goodwill on which R-Ladies is founded, or face expulsion/a penalty of any form, at the discretion of the leadership team.

Full community guidelines are found here: https://github.com/rladies/starter-kit/wiki

PPS Photos, Films and all other media/recordings:
Photos, Films, and all other media/recordings: photographs and/or video/other media will be taken at events held by this community. By taking part in an R-Ladies Utrecht event you grant the community organizers full rights to use the images resulting from the photography/video filming/media, and any reproductions or adaptations of the images for publicity, fundraising or other purposes to help achieve the community’s aims. This might include (but is not limited to), the right to use them in their printed and online publicity, social media, press releases and funding applications. If you do not wish to be recorded in these media please inform a community organizer.

Upcoming events (1)

Boook Club "Machine Learning with R" - Clustering

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Join us for our new online boookclub session discussing ‘Hands-On Machine Learning with R’ by Bradley Boehmke and Brandon Greenwell!

Monday March 27, we will talk about pt IV of the book: Clustering! From the book:

In PART III of this book we focused on methods for reducing the dimension of our feature space (p). The remaining chapters concern methods for reducing the dimension of our observation space (n); these methods are commonly referred to as clustering. K-means clustering is one of the most commonly used clustering algorithms for partitioning observations into a set of k groups (i.e. k clusters), where k is pre-specified by the analyst. k-means, like other clustering algorithms, tries to classify observations into mutually exclusive groups (or clusters), such that observations within the same cluster are as similar as possible (i.e., high intra-class similarity), whereas observations from different clusters are as dissimilar as possible (i.e., low inter-class similarity). In k-means clustering, each cluster is represented by its center (i.e, centroid) which corresponds to the mean of the observation values assigned to the cluster. The procedure used to find these clusters is similar to the k-nearest neighbor (KNN) algorithm discussed in Chapter 8; albeit, without the need to predict an average response value.

These chapters (20-22) will be presented by one of the organizers of R Ladies Den Bosch: Martine.

The attentive reader might notice that we skipped part III of the book: dimension reduction. This part contains chapter 17 Principal Components Analysis, 18 Generalised Low Rank models, and 19 Autoencoders. If you are looking for a fun little challenge and would like to present one or more of these chapters, please send us a message!

Past events (14)

Book Club "Machine Learning with R" - Interpretable Machine Learning

This event has passed

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