[May 4, 14:30] Tomáš Kuzin: Sensor Failure Detection and Prediction

Place: TH:A-1347

Sensors are collecting huge amounts of data, but besides the state of observed phenomena itself, what else can we determine from the collected measurements? Aim of this presentation is to show some methods currently used to detect anomalies in sensory data sets, which may suggest potential sensor failure and use such a knowledge for predictive maintenance.

Presentation slides are available here and video here.

[April 20, 14:00] Martin Barus: Bayesian Networks

Place: TH:A-1347

Bayesian networks, also known as belief networks, belong to the family of probabilistic graphical models. Combining principles from graph theory, probability theory, computer science, and statistics, BNs are used for reasoning under uncertainty as they infer conditional probability distributions of unobserved variables based on the evidence set (observed variables) and joint probability distribution. They have been successfully applied in victim identification, fraud detection, spam filtering, cancer risk modeling etc.

Presentation slides are available here and video here.

[November 27, 14:30] David Veselý and Tomáš Borovička: Machine learning in Agriculture

Place: TH:A-1347

On modern automated dairy farms, cows are continuously monitored and data is analyzed in real time. The aim is to reduce the need of farmer's attention to limited regular events or potential exceptions.
In this presentation we will talk about practical applications of machine learning and data mining techniques in the field of agriculture, particularly in automated farming. We also offer several research topics, that you could choose to collaborate with us.
David, currently a second year Master’s student in Knowledge Engineering, started collaboration with Lely (innovators in agriculture) in June 2014 through the Portal for Cooperation with Industry. He got enthusiastic about what we do and continued with his project as an intern and spent 3 months in the Netherlands. He is now working on same project also as his Master’s thesis.

[December 10, 12:45] Jan Černý: Precise predictions automated by machine learning

Place: T2:C3-54 (FEL)

In Modgen, we developed a technology that can generate data tailored precise predictive models. Computational intelligence and machine learning methods helped us automate the algorithm construction, model selection and maintenance. Evolutionary algorithms, genetic programming, coevolution, predictive models, ensemble systems, meta learning - these technologies are behind the Modgen predictive engine. Come and learn how research prototypes are transformed into the solutions powering our products.

[March 30, 15:00] Tomáš Šabata: Kalman filter

Place: TH:A-1347

The Kalman filter is one of the most celebrated and popular data fusion algorithm in the field of information processing. The most famous early use of the Kalman filter was in the Apollo navigation computer that took Neil Armstrong to the moon, and (most importantly) brought him back. Today, Kalman filters are at work in every satellite navigation device, every smart phone, and many computer games.

Presentation slides are available here and video here.

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