Vladimir Alekseichenko podczas AIMeetup #3 w Krakowie organizowanego przez 2040.io opowiadał o tym jak uczenie maszynowe istotnie wpływa na nasze życie już teraz i jak jeszcze bardziej wpłynie życie na naszych dzieci czy wnuków.

1. Uczenie maszynowe
Vladimir Alekseichenko
„rocket science” czy chleb powszedni?

2. Zmiany w czasie

6. 10min na jeden
36 500 000 minut
~70 lat

8. Kierowca vs Mechanik

9. dataworkshop.eu

10. Bike Sharing Demand
Zadnie - kaggle
Rozwiązanie - github.com/dataworkshop

11. Understand
Business & Data
Read and explore data
Feature Engineering
Create a new ones based on already exists
Feature Selection
Select only useful features
Model Selection
Find the best model(s) model
A
model
B
model
C
model
D
model
E
Tuning
Hyperparameters
Find the best hyperparameters for given model
Ensemble Modeling
Combine few models into one more better
x0.6 x0.4+
mode
l B
mode
l E
datetime season temp count
2011-01-01 08:32:02 1 9.23 5
2012-04-02 12:10:00 2 18.78 32
2012-08-07 15:47:01 3 15.45 15
datetime season temp hour day month … count count_log
2011-01-01
08:32:02 1 9.23 8 1 1 … 5 1.609
2012-04-02
12:10:00 2 18.78 12 2 4 … 32 3.466
2012-08-07
15:47:01 3 15.45 15 7 8 … 15 2.708
mode
l B
mode
l E
datetime season temp hour day month … count count_log
2011-01-01
08:32:02 1 9.23 8 1 1 … 5 1.609
2012-04-02
12:10:00 2 18.78 12 2 4 … 32 3.466
2012-08-07
15:47:01 3 15.45 15 7 8 … 15 2.708

12. Understand
Business & Data
Read and explore data
Feature Engineering
Create a new ones based on already exists
Feature Selection
Select only useful features
Model Selection
Find the best model(s) model
A
model
B
model
C
model
D
model
E
Tuning
Hyperparameters
Find the best hyperparameters for given model
Ensemble Modeling
Combine few models into one more better
x0.6 x0.4+
mode
l B
mode
l E
datetime season temp count
2011-01-01 08:32:02 1 9.23 5
2012-04-02 12:10:00 2 18.78 32
2012-08-07 15:47:01 3 15.45 15
datetime season temp hour day month … count count_log
2011-01-01
08:32:02 1 9.23 8 1 1 … 5 1.609
2012-04-02
12:10:00 2 18.78 12 2 4 … 32 3.466
2012-08-07
15:47:01 3 15.45 15 7 8 … 15 2.708
mode
l B
mode
l E
datetime season temp hour day month … count count_log
2011-01-01
08:32:02 1 9.23 8 1 1 … 5 1.609
2012-04-02
12:10:00 2 18.78 12 2 4 … 32 3.466
2012-08-07
15:47:01 3 15.45 15 7 8 … 15 2.708

13. Zrozum Biznes i Dane
(understand business and data)

15. Dni robocze

16. Weekend

17. Understand
Business & Data
Read and explore data
Feature Engineering
Create a new ones based on already exists
Feature Selection
Select only useful features
Model Selection
Find the best model(s) model
A
model
B
model
C
model
D
model
E
Tuning
Hyperparameters
Find the best hyperparameters for given model
Ensemble Modeling
Combine few models into one more better
x0.6 x0.4+
mode
l B
mode
l E
datetime season temp count
2011-01-01 08:32:02 1 9.23 5
2012-04-02 12:10:00 2 18.78 32
2012-08-07 15:47:01 3 15.45 15
datetime season temp hour day month … count count_log
2011-01-01
08:32:02 1 9.23 8 1 1 … 5 1.609
2012-04-02
12:10:00 2 18.78 12 2 4 … 32 3.466
2012-08-07
15:47:01 3 15.45 15 7 8 … 15 2.708
mode
l B
mode
l E
datetime season temp hour day month … count count_log
2011-01-01
08:32:02 1 9.23 8 1 1 … 5 1.609
2012-04-02
12:10:00 2 18.78 12 2 4 … 32 3.466
2012-08-07
15:47:01 3 15.45 15 7 8 … 15 2.708

18. Wytworzenie cech
(feature engineering)
• ilościowe => od 1 do 10, 11 do 20…
• daty => dzień, miesiąc, rok, godzina, czy weekend…
• kategorii/jakościowe (czerwony, zielony, biały)
• przypisać identyfikator liczbowy (1, 2, 3)
• stworzyć n-kolumn binarnych (jest czerwony? itd)
• prawdopodobieństwa ze zmienną docelową

20. Understand
Business & Data
Read and explore data
Feature Engineering
Create a new ones based on already exists
Feature Selection
Select only useful features
Model Selection
Find the best model(s) model
A
model
B
model
C
model
D
model
E
Tuning
Hyperparameters
Find the best hyperparameters for given model
Ensemble Modeling
Combine few models into one more better
x0.6 x0.4+
mode
l B
mode
l E
datetime season temp count
2011-01-01 08:32:02 1 9.23 5
2012-04-02 12:10:00 2 18.78 32
2012-08-07 15:47:01 3 15.45 15
datetime season temp hour day month … count count_log
2011-01-01
08:32:02 1 9.23 8 1 1 … 5 1.609
2012-04-02
12:10:00 2 18.78 12 2 4 … 32 3.466
2012-08-07
15:47:01 3 15.45 15 7 8 … 15 2.708
mode
l B
mode
l E
datetime season temp hour day month … count count_log
2011-01-01
08:32:02 1 9.23 8 1 1 … 5 1.609
2012-04-02
12:10:00 2 18.78 12 2 4 … 32 3.466
2012-08-07
15:47:01 3 15.45 15 7 8 … 15 2.708

21. Selekcja cech
(feature selection)
• Czym mniej tym lepiej (prostszy model)
• Zostawić najbardziej wartościowe (idealnie jedna :)
• Cechy (zazwyczaj) są zależny, więc trzeba uważać… (sprawdzać empirycznie)
• Szybciej

22. Variance
Univariate
Recursive

23. xgbfir
https://github.com/limexp/xgbfir

24. Understand
Business & Data
Read and explore data
Feature Engineering
Create a new ones based on already exists
Feature Selection
Select only useful features
Model Selection
Find the best model(s) model
A
model
B
model
C
model
D
model
E
Tuning
Hyperparameters
Find the best hyperparameters for given model
Ensemble Modeling
Combine few models into one more better
x0.6 x0.4+
mode
l B
mode
l E
datetime season temp count
2011-01-01 08:32:02 1 9.23 5
2012-04-02 12:10:00 2 18.78 32
2012-08-07 15:47:01 3 15.45 15
datetime season temp hour day month … count count_log
2011-01-01
08:32:02 1 9.23 8 1 1 … 5 1.609
2012-04-02
12:10:00 2 18.78 12 2 4 … 32 3.466
2012-08-07
15:47:01 3 15.45 15 7 8 … 15 2.708
mode
l B
mode
l E
datetime season temp hour day month … count count_log
2011-01-01
08:32:02 1 9.23 8 1 1 … 5 1.609
2012-04-02
12:10:00 2 18.78 12 2 4 … 32 3.466
2012-08-07
15:47:01 3 15.45 15 7 8 … 15 2.708

25. Dobór Modelu
(model selection)
• Linear
• Decision Tree
• Random Forest
• Gradient Boosting
• Neural Network

26. Linear
https://github.com/dataworkshop/model_evaluation/blob/master/step1-regression.ipynb

27. Decision Tree
http://xgboost.readthedocs.io/en/latest/model.html

28. Ensemble trees
http://xgboost.readthedocs.io/en/latest/model.html

29. Ensemble trees
• Bagging (bootstrap aggregation)
• Random Forest
• Extra Trees
• Boosting
• Gradient Boosting

30. XGBoost
(Extreme Gradient Boosting)
“When in doubt, use
xgboost”
Owen Zhang

31. Wybór modelu
(model selection)

32. Understand
Business & Data
Read and explore data
Feature Engineering
Create a new ones based on already exists
Feature Selection
Select only useful features
Model Selection
Find the best model(s) model
A
model
B
model
C
model
D
model
E
Tuning
Hyperparameters
Find the best hyperparameters for given model
Ensemble Modeling
Combine few models into one more better
x0.6 x0.4+
mode
l B
mode
l E
datetime season temp count
2011-01-01 08:32:02 1 9.23 5
2012-04-02 12:10:00 2 18.78 32
2012-08-07 15:47:01 3 15.45 15
datetime season temp hour day month … count count_log
2011-01-01
08:32:02 1 9.23 8 1 1 … 5 1.609
2012-04-02
12:10:00 2 18.78 12 2 4 … 32 3.466
2012-08-07
15:47:01 3 15.45 15 7 8 … 15 2.708
mode
l B
mode
l E
datetime season temp hour day month … count count_log
2011-01-01
08:32:02 1 9.23 8 1 1 … 5 1.609
2012-04-02
12:10:00 2 18.78 12 2 4 … 32 3.466
2012-08-07
15:47:01 3 15.45 15 7 8 … 15 2.708

33. Dobór hiperparametrów
(tuning hyperparameters)
• Grid Search
• Random Search
• Bayesian

34. hyperopt

35. Understand
Business & Data
Read and explore data
Feature Engineering
Create a new ones based on already exists
Feature Selection
Select only useful features
Model Selection
Find the best model(s) model
A
model
B
model
C
model
D
model
E
Tuning
Hyperparameters
Find the best hyperparameters for given model
Ensemble Modeling
Combine few models into one more better
x0.6 x0.4+
mode
l B
mode
l E
datetime season temp count
2011-01-01 08:32:02 1 9.23 5
2012-04-02 12:10:00 2 18.78 32
2012-08-07 15:47:01 3 15.45 15
datetime season temp hour day month … count count_log
2011-01-01
08:32:02 1 9.23 8 1 1 … 5 1.609
2012-04-02
12:10:00 2 18.78 12 2 4 … 32 3.466
2012-08-07
15:47:01 3 15.45 15 7 8 … 15 2.708
mode
l B
mode
l E
datetime season temp hour day month … count count_log
2011-01-01
08:32:02 1 9.23 8 1 1 … 5 1.609
2012-04-02
12:10:00 2 18.78 12 2 4 … 32 3.466
2012-08-07
15:47:01 3 15.45 15 7 8 … 15 2.708

36. Ansambl
(ensemble modeling)

38. Neuron

39. (Artificial) Neural Network

40. MNIST

41. Dane

42. Neural Network
Error: 1.60%

43. http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html

44. source

45. Wyzwania

46. Przeuczenie się
(overfitting)
http://mlwiki.org/index.php/Overfitting

47. Sprawdzian krzyżowy
(cross-validation)
http://blog.goldenhelix.com/bchristensen/cross-validation-for-genomic-prediction-in-svs/

48. Kreatywność jest wiele warta

49. https://techcrunch.com/2016/11/19/how-data-science-and-rocket-science-will-get-humans-to-mars

50. source
Fala już idzi…
czy jesteś gotów?

51. Dziękuję
@slon1024
hello@vova.me
dataworkshop.eu