Лекция одного из самых известных в России специалистов по машинному обучению Дмитрия Ветрова, который руководит департаментом больших данных и информационного поиска на факультете компьютерных наук, работающим во ВШЭ при поддержке Яндекса.

1. Mathematics of Big Data
Dmitry P. Vetrov
Head of Big Data and Information Retrieval Department,
Faculty of Computer Science, HSE.

2. Outline
• Intro to Machine learning
• Big Data specifics
• Bayesian framework and its extensions
• Learning from incomplete data
• Deep learning
• Stochastic optimization
• Tensor decompositions

3. Bayesian methods research group
Founded in 2007. Currently consists of 8 students, 5 PhD
students, 1 researcher and 1 associate professor.

4. Bayesian methods research group

5. What is machine learning?

6. Simple example

7. Areas of application

8. Stages

9. Entering the Age of Big Data
Data
Computational
speed

10. First Steps towards Mathematics of
Big Data

11. Bayesian Framework

12. Bayesian Learning and Inference

13. Advantages of Bayesian inference

14. Graphical Models

15. Application

16. Incomplete data

17. Incomplete data

18. EM algorithm

19. General idea of SVM

20. Latent variable SVM

21. Semantic image segmentation

22. Weak annotation

23. Example: Latent Dirichlet
Allocation

24. LDA: model

25. Deep learning

26. Secret of Success of Neural Nets

27. Stochastic Optimization

28. Advanced Stochastic Optimization

29. Tensor perspective

30. Advantages of TT Decomposition

31. Word2vec project

32. A Surprising effect

33. Latent semantic model

34. Results
Computer can now assign different semantic representations to different
occurrences of same word depending on the context

35. Conclusion

36. References
• (Osokin15) A. Osokin, D. Vetrov. Submodular Relaxation for
Inference in Markov Random Fields. In IEEE TPAMI, 2015.
• (Novikov14) A. Novikov, A. Rodomanov, A. Osokin, D. Vetrov. Putting
MRF on a Tensor Train. In ICML2014
• (Bartunov14) S. Bartunov, D. Vetrov. Variational Inference for
Sequential Distance Dependent Chinese Restaurant Process. In
ICML2014
• (Shapovalov15) R. Shapovalov, A. Osokin, D. Vetrov, P. Kohli. Multi-
utility Learning: Structured-output Learning with Multiple
Annotation-specific Loss Functions. In EMMCVPR15
• (Kirillov14) A. Kirillov, K. Lobacheva, M. Gavrikov, A. Osokin, D.
Vetrov. Deep Part-Based Shape Model with Latent Variables. In
GraphiCon14