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Data coffee - Support vector machine usage with complex data
The document outlines a meetup group in Bratislava focused on exploring data science, artificial intelligence, and machine learning, welcoming participants of all skill levels. It includes discussions on machine learning techniques, algorithms, and applications in real-world scenarios, as well as insights into current trends in IT. The information also emphasizes collaboration and networking among enthusiasts in the field.
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Data coffee - Support vector machine usage with complex data
- 1. Meetup Bratislava This isa group for anyone who wants to explore the area of data science, artificial intelligence, machine learning. All skill and experience levels are welcome. I started this group to meet other outdoor enthusiasts of areas as: • build systems that use machine learning techniques in Clojure, Java, Node.js • understand machine learning problems such as regression, classification, and clustering • discover the data structures used in machine learning techniques such as artificial neural networks and support vector machines • implement machine learning algorithms in real scientific or business cases • learn more about software libraries to build machine learning systems • discover techniques to improve and debug solutions built on machine learning techniques • use machine learning techniques in a cloud architecture for the modern Web branislav.majernik@oracle.com DataData
- 2. Meetup Bratislava Vážení priaznivcimoderného IT, Prijmite naše pozvanie na dobrú kávu s crossisantom v priestoroch kaviarne KORZO na Hviezdoslavovom námestí 3 v Starom Meste, dňa 14.2.2018 od 9:00 – 11:00. Naštartujeme deň diskusiou o aktuálnych trendoch v informačných technológiách, o data science, machine learning a IT s tým súvisiacim. • Support Vector Machine – prečo je úspešná v segmentácii komplikovaného zákazníka • Markov chains a ich využitie v predikcii správania sa zákazníka Ak ste sa v téme nenašli, nezúfajte, nasledovať bude: Lambda Coffee - venovaný funkcionálnej paradigme a technológiám. Registrácia na adrese: branislav.majernik@oracle.com klaudia.blaskovicova@oracle.com DataData
- 3. What is datascience ?
- 4. What is datascience? Tasks of regression, interpolation
- 5. What is datascience? Tasks of classification
- 6. What is datascience? How to learn computer a new function without explicit programming
- 7. Data science needs Platform Mathematical NativeLanguage Speed Data Structures Language Constructs Data Connectivity End-2-End Quality Control Interactive Environment Visualization Linear Algebra Statistics Machine Learning Optimizers R, Mathlab, Mathematica C++, Java, Python Excel
- 8. Data science needs Productive and fun Portability, good parts of JVM ( ) REPL – interactive experiments Functional programing DSL's with composable abstractions Data is code, code is data and deployment in ... spread of platform, on the fly profiling, inlining, loop-unrolling, de-opt/reopt, escape analysis, dead code elimination, proven GC
- 9. Support Vector Machine Tasksof classification Source of this picture: https://med.nyu.edu/chibi/sites/default/files/chibi/Final.pdf
- 10. Support Vector Machine Tasksof classification Source of this picture: https://med.nyu.edu/chibi/sites/default/files/chibi/Final.pdf
- 11. Support Vector Machine Tasksof classification Source of this picture: https://med.nyu.edu/chibi/sites/default/files/chibi/Final.pdf
- 12. Support Vector Machine Tasksof classification Source of this picture: https://med.nyu.edu/chibi/sites/default/files/chibi/Final.pdf
- 13. Support Vector Machine Tasksof classification and regression
- 14. Support Vector Machine Tasksof classification and regression Source of this picture: https://med.nyu.edu/chibi/sites/default/files/chibi/Final.pdf
- 15. Support Vector Machine Tasksof classification and regression Source of this picture: http://web.mit.edu/6.034/wwwbob/svm-notes-long-08.pdf
- 16. Support Vector Machine Tasksof classification and regression Source of this picture: http://web.mit.edu/6.034/wwwbob/svm-notes-long-08.pdf
- 17. Support Vector Machine Tasksof classification and regression Source of this picture: http://www.robots.ox.ac.uk/~az/lectures/ml/lect3.pdf
- 18. Support Vector Machine Tasksof classification Source of this picture: http://www.robots.ox.ac.uk/~az/lectures/ml/lect3.pdf
- 19. Support Vector Machine Tasksof classification Source of this picture: http://www.robots.ox.ac.uk/~az/lectures/ml/lect3.pdf
- 20. Support Vector Machine Tasksof classification Source of this picture: http://scikit-learn.org/stable/_images/sphx_glr_plot_rbf_parameters_001.png
- 21. Support Vector Machine Tasksof regression Source of this picture: http://www.robots.ox.ac.uk/~az/lectures/ml/lect3.pdf
- 22. Support Vector Machine Tasksof regression Source of this picture: http://www.robots.ox.ac.uk/~az/lectures/ml/lect3.pdf
- 23. Support Vector Machine Tasksof regression Source of this picture: https://www.svm-tutorial.com/2014/10/support-vector-regression-r/
- 24. Support Vector Machine Correspondencewith Radial Basis Neural Network Source of this picture: http://mccormickml.com/2013/08/15/radial-basis-function-network-rbfn-tutorial/
- 25. Support Vector Machine Withgenetic programing evolution Source of this picture: https://github.com/ssusnic/Machine-Learning-Flappy-Bird
- 26. Markov chain When probabilitydepend on previous outcome
- 27. Hidden Markov chain Whenprobability depend on previous outcome and is hidden from observable state
- 28. Hidden Markov chain Whenprobability depend on previous outcome and is hidden from observable state
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