Machine learning: How it can help your business - Microsoft Future Decoded - Budapest, March 2018

Transcript

1. Machine Learning: How It Can Help Your Business Szilárd Pafka, PhD Chief Scientist, Epoch (USA) Microsoft Future Decoded, Budapest March 2018
3. Disclaimer: I am not representing my employer (Epoch) in this talk I cannot confirm nor deny if Epoch is using any of the methods, tools, results etc. mentioned in this talk
5. Source: Andrew Ng
7. y = f(x) “Learn” f from data Source: Hastie etal, ESL 2ed
8. Machine Learning linear/logistic regression decision trees neural networks support vector machines random forests gradient boosting deep learning neural networks
9. Machine Learning linear/logistic regression (early 1900s/60s) decision trees (60s/80s) neural networks (60s/80s) support vector machines (90s) random forests (90s) gradient boosting (90s) deep learning neural networks (2000s)
11. data mining Source: Szilard Pafka
12. data science Source: Szilard Pafka
13. data science Source: Szilard Pafka
14. CRISP-DM, 1999
15. data $$$
16. How?
19. Source: Andrew Ng
23. Source: @iamdevloper (twitter)
27. structured/tabular data: GBM (or RF) very small data: LR very large sparse data: LR with SGD images/videos, speech: DL
28. structured/tabular data: GBM (or RF) very small data: LR very large sparse data: LR with SGD images/videos, speech: DL better answer: it depends
29. structured/tabular data: GBM (or RF) very small data: LR very large sparse data: LR with SGD images/videos, speech: DL better answer: it depends alternative answer: try them all
30. structured/tabular data: GBM (or RF) very small data: LR very large sparse data: LR with SGD images/videos, speech: DL better answer: it depends alternative answer: try them all extra accuracy: combine them (ensembles)
33. 10x
37. ML training: lots of CPU cores lots of RAM limited time
43. Source: Szilard Pafka
45. Random forest GBM GBM + cross validation GBM + hyperparameter tuning Logistic regression Neural Nets / Deep Learning Ensembles
52. Backup Slides
54. 10x
58. Source: Szilard Pafka: 10 Pitfalls in Data Science, LA Data Science Meetup, February, 2014