Wednesday, April 13, 2016

Paris Machine Learning #11 S3: Rogue Waves, Dataiku, eLum, Human Resources, DNN on Matlab, Data and Cars, The Great Convergence

The program has changed slightly and will probably change until later today. Due to the flu, the Mars presentation is postponed.

Interested in making a presentation tonight ? go fill this form here, we'll get back to you shortly.

 Le meetup sera à l'Etoile Saint Honoré au 21/25 rue de Balzac ( Merci a Xebia d'organiser cette événement. Pour ce meetup nous devrions avoir (programme qui changera): 

 Laurence Vachon will briefly present Mathworks' Mission On Mars Robot Challenge 2016. Think of it as a Kaggle for robots. In a different area,Themis Sapsis will show how to predict rogue/killer waves using a new technique based on ML. Martin Prillard will talk to us about company cultures and Machine Learning. Amine El Helou will present the new Deep Learning libraries within Matlab. Florent Pignal will finally briefly mention how his start-up wants to make use of data from computers that are already in cars.

Interested in making a presentation tonight ? go fill this form here, we'll get back to you shortly.

Here are the presentations:

  • Matthieu Blanc ou Yoann Benoit, Presentation Xebia

• Martin Prillard, Talentoday, How psychometrics and machine learning can identify corporate cultures and business success factors

Comment la psychométrie et le machine learning peuvent identifier les cultures d'entreprise et les facteurs de réussite professionnels.
Amine El Helou, Deep Learning in Matlab.

Deep learning is becoming ubiquitous. In this example we are going to train a Convolutional Neural Network from scratch in order to classify the popular dataset CIFAR-10 using MATLAB. 
Igor Carron, "The Great Convergence" or How ML/DL is disrupting sensor design.

I describe through three examples how sensor design is being disrupted by new Machine Learning Techniques.

• Themis Sapsis (MIT) Robust prediction of extreme wave events in realistic seas

The objective of this work is the development of robust computational algorithms for the prediction of the location and time of rare and extreme wave events in realistic seas. Today’s algorithms for the prediction of rare events rely on the direct computation of the wave field by accurately solving the governing wave equations. This is a computationally very demanding task that requires a large number of computational nodes working in parallel for long times, even for a few minutes of prediction. In addition, due to the chaotic nature of the wave dynamics, the results are very sensitive to noise. The latter is inevitable during the measurement/scanning process. These properties make these direct numerical simulations impractical for real-time prediction. We use a radically different approach for the prediction of rare events in water waves. Instead of trying to accurately solve the full equations, we analyze their dynamical properties and combine them with statistics that characterize the wave field. Specifically, we use the wave equations to understand which wave-groups (i.e. with what shape) are dynamically prone on evolving to extreme waves in the near future. This analysis gives a wide range of possible waves that indeed can evolve to extreme events. However, the sea state conditions are those that define the probability for the formation of each of those critical wave-groups. Combining these two pieces of information we identify the pattern of waves that i) has the highest likelihood to appear in given sea state conditions, and ii) if it appears it will evolve into an extreme wave. Having identified the critical wave-group, the next step is to use scanning technologies of the surrounding wave field in order to identify where this wave-group may appear. This process requires trivial computational effort and is very robust to measurement noise.

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