The document outlines a machine learning agenda aimed at encouraging widespread use of the technology, addressing common misconceptions such as perceived complexity and high costs. It presents various applications of machine learning, including diagnosing tuberculosis, classifying products, and crime clustering, utilizing different algorithms like neural networks and random forests. The document emphasizes accessibility through online courses and tools, suggesting that machine learning is achievable for everyone.

1. Machine Learning
for Everyone

2. Agenda
Goal: Encourage you to use Machine Learning… today!
‣ Aboutme
‣ MachineLearning
Misconceptions
Concepts
Problems andAlgorithms
‣ ForEveryone!

3. About me
Electronics Engineering, Software
Development, Data Science… Why not?

4. Neural TB
Tool that aids in the
diagnosis ofTuberculosis
using Neural Networks

5. Neural Ringer
Algorithm for online
electron/jet
discrimination forthe
ATLAS detector at CERN
using Neural Networks

6. djBrazil
Intelligent music platform
specialized in Brazilian
music

7. Jigsaw Dots
Interactive exploratory
visualization of
employees based on their
skills

8. Higgs Challenge

9. Machine
Learning
It is all about learning

10. Misconceptions
Too difficult
Big upfront investments
Needs supercomputers
Only for PhDs from MIT
Data Silo
Takes too long to pay off
ƃ

11. Reality
♥

12. Feature Extraction
Item {
Feature 1
Feature 2
Feature 3
…
Feature N
Feature2
0
8
16
24
32
40
Feature 1
0 10 20 30 40 50 60 70 80 90

13. Supervised Learning
23, 45, 67, 78
12, 48, 68, 22
…
34, 58, 77, 19
3
2
…
5
20, 39, 59, 68 3
♥
Items Features Labels Algorithm
New Item PredictionFeatures Model

14. Supervised Learning Algorithms
K-Nearest Neighbors Neural Networks
DecisionTree Random Forest

15. Regression
921 37
23 2487 1541
21 2121 21
?

16. Boston housing prices
Prediction of house
prices at Boston suburbs
based on census data
using Linear Regression

17. Classification
BA B
A AB AB
A AB B
?

18. Detecting particles
Online electron detection
based on more than 1500
detector cells using
Neural Networks
(GeV)TE
0 10 20 30 40 50 60 70 80
o(%)a
~
Prob.deRejeic
20
30
40
50
60
70
80
90
100
Ringer
T2Calo
smicosoC
η
-2 -1 0 1 2
o(%)a
~
Prob.deRejeic
80
85
90
95
100
Ringer
T2Calo
φ
-3 -2 -1 0 1 2 3
o(%)a
~
Prob.deRejeic
90
92
94
96
98
100
Ringer
T2Calo

19. Classifying products
Product classification on
9 different classes based
on 90 numerical features
usingAmazon Machine
Learning

20. Diagnosing Tuberculosis
Tuberculosis diagnosis
based on patients
questionnaires using
Neural Networks

21. Unsupervised Learning
23, 45, 67, 78
12, 48, 68, 22
…
34, 58, 77, 19
20, 39, 59, 68
♥
Items Features Algorithm
New Item
Better
RepresentationFeatures Model

22. Unsupervised Learning Algorithms
K-Means Self-Organising Maps t-SNE

23. Clustering

24. Clustering crime
Crime clusters based on
information from the San
Francisco Police
Department Crime
Incident Reporting
system using K-means

25. Dimensionality Reduction

26. Visualizing employees
Visualization of 2000+
employees described by
200+ skills after reducing
dimensionality using the
t-SNE algorithm

27. For everyone!
You can do it!

28. Massive Online Open Courses

29. Open Source Tools

30. Pay-as-you-go
Amazon
Machine Learning
Google Cloud
Machine Learning
Azure
Machine Learning

31. Cheat sheets

32. Kaggle
Sponsors
Workflow Reasons

33. Code Snippet
>>> classifier = RandomForestClassifier().fit(features, labels)
>>> prediction = classifier.predict(new_features)
23, 45, 67, 78
12, 48, 68, 22
…
34, 58, 77, 19
A
B
…
B
20, 39, 59, 68 A
♥
Items Features Labels Algorithm
New Item PredictionFeatures Model

34. Thank you.
Twitter: @dhianadeva
Email: dhiana@spotify.com