Data coffee - Support vector machine usage with complex data
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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.
Data coffee - Support vector machine usage with complex data
- 1. Meetup Bratislava This is a 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 priaznivci moderného 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 data science ?
- 4. What is data science? Tasks of regression, interpolation
- 5. What is data science? Tasks of classification
- 6. What is data science? How to learn computer a new function without explicit programming
- 7. Data science needs Platform Mathematical Native Language 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 Tasks of classification Source of this picture: https://med.nyu.edu/chibi/sites/default/files/chibi/Final.pdf
- 10. Support Vector Machine Tasks of classification Source of this picture: https://med.nyu.edu/chibi/sites/default/files/chibi/Final.pdf
- 11. Support Vector Machine Tasks of classification Source of this picture: https://med.nyu.edu/chibi/sites/default/files/chibi/Final.pdf
- 12. Support Vector Machine Tasks of classification Source of this picture: https://med.nyu.edu/chibi/sites/default/files/chibi/Final.pdf
- 13. Support Vector Machine Tasks of classification and regression
- 14. Support Vector Machine Tasks of classification and regression Source of this picture: https://med.nyu.edu/chibi/sites/default/files/chibi/Final.pdf
- 15. Support Vector Machine Tasks of classification and regression Source of this picture: http://web.mit.edu/6.034/wwwbob/svm-notes-long-08.pdf
- 16. Support Vector Machine Tasks of classification and regression Source of this picture: http://web.mit.edu/6.034/wwwbob/svm-notes-long-08.pdf
- 17. Support Vector Machine Tasks of classification and regression Source of this picture: http://www.robots.ox.ac.uk/~az/lectures/ml/lect3.pdf
- 18. Support Vector Machine Tasks of classification Source of this picture: http://www.robots.ox.ac.uk/~az/lectures/ml/lect3.pdf
- 19. Support Vector Machine Tasks of classification Source of this picture: http://www.robots.ox.ac.uk/~az/lectures/ml/lect3.pdf
- 20. Support Vector Machine Tasks of classification Source of this picture: http://scikit-learn.org/stable/_images/sphx_glr_plot_rbf_parameters_001.png
- 21. Support Vector Machine Tasks of regression Source of this picture: http://www.robots.ox.ac.uk/~az/lectures/ml/lect3.pdf
- 22. Support Vector Machine Tasks of regression Source of this picture: http://www.robots.ox.ac.uk/~az/lectures/ml/lect3.pdf
- 23. Support Vector Machine Tasks of regression Source of this picture: https://www.svm-tutorial.com/2014/10/support-vector-regression-r/
- 24. Support Vector Machine Correspondence with Radial Basis Neural Network Source of this picture: http://mccormickml.com/2013/08/15/radial-basis-function-network-rbfn-tutorial/
- 25. Support Vector Machine With genetic programing evolution Source of this picture: https://github.com/ssusnic/Machine-Learning-Flappy-Bird
- 26. Markov chain When probability depend on previous outcome
- 27. Hidden Markov chain When probability depend on previous outcome and is hidden from observable state
- 28. Hidden Markov chain When probability depend on previous outcome and is hidden from observable state