Prediction problems in geographic information science and transportation are often motivated by the possibility to enhance operational efficiency and thereby reduce emissions. Examples range from predicting car sharing demand for relocation planning to forecasting traffic congestion for navigation purposes. However, conventional accuracy metrics ignore the spatial distribution of the errors, despite its relevance for operations. Here, we put forward a spatially aware evaluation metric and loss function based on Optimal Transport (OT). Our framework leverages partial OT and can minimize relocation costs in any spatial prediction problem. We showcase the advantages of OT-based evaluation over conventional metrics and further demonstrate the application of an OT loss function for improving forecasts of bike sharing demand and charging station occupancy.

To address the interlinked biodiversity and climate crises, we need an understanding of where species occur and how these patterns are changing. However, observational data on most species remains very limited, and the amount of data available varies greatly between taxonomic groups. We introduce the problem of predicting species occurrence patterns given (a) satellite imagery, and (b) known information on the occurrence of other species. To evaluate algorithms on this task, we introduce SatButterfly, a dataset of satellite images, environmental data and observational data for butterflies, which is designed to pair with the existing SatBird dataset of bird observational data. To address this task, we propose a general model, R-Tran, for predicting species occurrence patterns that enables the use of partial observational data wherever found. We find that R-Tran outperforms other methods in predicting species encounter rates with partial information both within a taxon (birds) and across taxa (birds and butterflies). Our approach opens new perspectives to leveraging insights from species with abundant data to other species with scarce data, by modelling the ecosystems in which they co-occur.

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"It was about machine learning on HPC (high performance computing) clusters.

After attending Edureka, Tableau classes, I feel much more confident in Tableau. Such is the learning curve which gives you great confidence. Edureka is a great help, especially for working professionals. It provides flexibility in all aspects which makes the learning experience awesome! We invite you to register for a FREE 2-day online workshop where you will learn the fundamentals of Machine Learning & AI in a live, interactive environment from an industry expert.

Do you have 3 / 4 years of coding experience? Do you want to upskill in Machine Learning, and network with some of Dublin's most talented women in the tech space? Workday is running a women in tech workshop for developers to come together for a hands-on introduction to Machine Learning, free of charge. Workday's Laura Uzcategui will be leading an'Introduction to Machine Learning' workshop, working through an example live on the evening for you to follow along with. So bring along your laptop and get ready to enter the world of Machine Learning!

Our Machine Learning experts will conduct a half day working session with your team to jointly brainstorm and explore different business problems within your organization and help arrive at a prioritized set of use cases towards the end of the session for maximum business benefit.

Paul Brabban from def_shef in Sheffield will talk us through implementing a classic machine learning algorithm. This is a workshop session, so do bring your laptop to play along if you can! Paul writes: Its simplicity, amongst other properties, make it a great introduction to machine learning algorithms. In the workshop, you'll implement a kNN classifier in the functional language of your choice, including training and testing against real data sets using k-fold cross validation. I'll help anyone who's having problems, and for FP experts who finish the implementation quickly, there'll be several suggestions for further exploration. If any of those terms are new to you, don't worry!

Here's some excellent news: Roughly 53% of statisticians are women. This is up from 5.8% in 1978. So it is with some surprise that this isn't common knowledge. Some say that it's the fact that women are not well represented at conferences, giving the mis-impression that women are not well represented in the industry. So how can we get more women involved in conferences?

Here's some excellent news: Roughly 53% of statisticians are women. So it is with some surprise that this isn't common knowledge. Some say that it's the fact that women are not well represented at conferences, giving the mis-impression that women are not well represented in the industry. So how can we get more women involved in conferences? When you think about a field that is all about data and crunching actual numbers, it's hard to believe that women are actually so prevalent.