This document provides an introduction to machine learning. It discusses how machine learning gives computers the ability to learn without being explicitly programmed. It also discusses how machine learning is used widely by major companies and has become integral to many businesses. Finally, it covers different machine learning techniques including supervised learning methods like classification, regression, and artificial neural networks as well as unsupervised learning methods like clustering.

1. An Introduction to
Machine Learning
Raveen Harith Perera GDG Sri Lanka

2. ”
Machine learning is a
Field of study that gives computers
the ability to learn without being
explicitly programmed
Arthur Samuel

3. Where do we have machine learning in use?
▹ All big companie use machine learning
▹ Machine learning and data sciences have become the key role
in every business
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4. Google in Machine Learning4

5. 5
Machine Learning
Supervised Unsupervised
Classification
(discrete)
Regression
(continuous)
Clustering
Artificial Neural Networks
Learning Vector Quantization
Support Vector Machines
Hidden Markov Models
K-Means
Self Organizing Maps
ASR Mining
Neural Gas

6. UNSUPERVISED LEARNING
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7. Unsupervised learning7
How would you divide
these animals ?
There are number of
features to consider
It will be based on what
groups you want

8. Unsupervised learning
What kind of features can we use ?
Hair color
Number of legs
Height
Weight
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9. Unsupervised learning - Clustering9
Cluster 1
Cluster 2
outliers

10. Unsupervised learning - K-Means Clustering10

11. SUPERVISED LEARNING
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Labeled Inputs + Training
Regression Models and Classification

12. Supervised learning - Regression & Classification12

13. Supervised Learning - Regression
Regression is used on continuous data
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Free time
age

14. Supervised Learning - Regression
Regression is used on continuous data
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15. Supervised Learning - Regression
Overfitting and Underfitting
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underfit good Overfit

16. Supervised learning - Artificial Neural Networks16
Similar to how our brains learn
Knowledge is kept on weights
Weights are updated to reduce errors
at each learning stage
Backpropagation methods are used
to learn the network

17. Supervised learning - Artificial Neural Networks17
Age
Platelet count
White blood
cell count
Dengue
positive or Negative

18. Supervised learning - Artificial Neural Networks18
▹ Complex image classifications (Deep
Learning + Convolutional NN)
▹ Natural Language Processing
▹ Computer Games
▹ Google Search, Facebook news feed,
Youtube recomendations

19. Deep Learning
Deep Neural Networks
▹ Convolutional
Neural Networks
▹ Deep Recurrent
Neural Networks
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20. Deep Learning20
▹ Deep learning research dates back to about 20
years from now
▹ Implementation was bounded by
▸ Computation power
▸ Large datasets

21. Some Applications
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22. Image Processing - Finding Objects in an Image22
Early methods use Template matching and SIFT

23. Image Processing - Finding Objects in an Image23
Convolutional Neural Networks

24. Image Processing - Describing an image24

25. Deep Dream25

26. Deep Dream26

27. PRISMA - Deep Neural Networks
Read more http://arxiv.org/pdf/1603.03417v1.pdf
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28. Baidu AI learns to compose through image recognition
https://www.youtube.com/watch?v=Ics9CjRSMfc
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29. Leading AI research companies29

30. Credits
Special thanks to all the people who
made and released these awesome
resources for free:
▹Presentation template by
SlidesCarnival
▹Photographs by
Startupstockphotos
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