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Introduction to Machine Learning
- 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 3
- 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 6
- 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 8
- 9. Unsupervised learning - Clustering9 Cluster 1 Cluster 2 outliers
- 10. Unsupervised learning - K-Means Clustering10
- 11. SUPERVISED LEARNING 11 Labeled Inputs + Training Regression Models and Classification
- 12. Supervised learning - Regression & Classification12
- 13. Supervised Learning - Regression Regression is used on continuous data 13 Free time age
- 14. Supervised Learning - Regression Regression is used on continuous data 14
- 15. Supervised Learning - Regression Overfitting and Underfitting 15 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 19
- 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 21
- 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 27
- 28. Baidu AI learns to compose through image recognition https://www.youtube.com/watch?v=Ics9CjRSMfc 28
- 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 30
