Machine Learning 101

Transcript

1. Machine Learning 101 Ali Akbar Septiandri Universitas Al Azhar Indonesia

2. Previously...

3. Cross Industry Standard Process for Data Mining (CRISP-DM)

4. Data Science Venn Diagram

5. What is the role of machine learning algorithms?

6. “Fundamentally, machine learning involves building mathematical models to help understand

   data.” - Jake VanderPlas

7. Tasks in Machine Learning 1. Predicting stock price 2. Differentiating

   cat vs. dog pictures 3. Spam identification 4. Community detection 5. Mimicking famous painting style 6. Mastering the game of go and chess 7. etc.

8. Task Categories 1. Supervised learning a. Predicting stock price b.

   Differentiating cat vs. dog pictures c. Spam identification 2. Unsupervised learning a. Community detection b. Mimicking famous painting style 3. Reinforcement learning a. Mastering the game of go and chess

9. - Iris Dataset - by R.A. Fisher (1936) - 4

   attributes: sepal length, sepal width, petal length, petal width - 3 labels: Iris Setosa, Iris Versicolour, Iris Virginica Let’s take an example dataset...
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15. Nearest Neighbour - Finding the closest reference - What does

    it mean by “closest”? - Humans comprehend visualisations very well - Can computers do the same?

16. At the lowest level, computers only understand 0 or 1

17. Euclidean Distance

18. Euclidean Distance

19. Are you sure?

20. 1. Find some k closest references 2. Use majority vote

    3. We need to compute pairwise distances k-Nearest Neighbours
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22. Conventional statistics can not do that

23. We need high computational power

24. What if we only want to see the subgroups in

    the data?

25. Clustering - Finding subgroups in the data - Your neighbours

    in the same housing complex regardless of their class - Unsupervised learning
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27. k-Means Clustering

28. k-Means Clustering 1. Uses Euclidean distance as well 2. k

    = number of clusters 3. Centroids to represent clusters
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32. Deep Learning

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34. Digit Recognition MNIST Dataset

35. Classifying objects from pictures [Krizhevsky, 2009]

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38. A neural network [Nielsen, 2016]

39. Logistic Regression y = σ(w 0 + w 1 x

    1 )

40. Predicting traffic jams from CCTV pictures

41. Mimicking famous paintings

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43. Other Machine Learning Algorithms

44. Naive Bayes

45. Decision trees

46. Linear regression with polynomial basis functions

47. “No free lunch”

48. Thank you