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Machine Learning (AIMS) - MT 2017 0. (My) Introduction to ML Varun - PowerPoint PPT Presentation

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Machine Learning (AIMS) - MT 2017 0. (My) Introduction to ML Varun Kanade University of Oxford November 6, 2017 Machine Learning in Action (Using https://www.betafaceapi.com/demo.html) 1 Machine Learning in Action (Using

  1. Machine Learning (AIMS) - MT 2017 0. (My) Introduction to ML Varun Kanade University of Oxford November 6, 2017

  2. Machine Learning in Action (Using https://www.betafaceapi.com/demo.html) 1

  3. Machine Learning in Action (Using https://www.betafaceapi.com/demo.html) 1

  4. Machine Learning in Action (Using https://www.betafaceapi.com/demo.html) 1

  5. What is machine learning? circa October 2016 2

  6. What is machine learning? circa October 2016 2

  7. What is machine learning? circa October 2017 3

  8. What is machine learning? circa October 2017 3

  9. What is machine learning? What is artificial intelligence? ‘‘Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child’s? If this were then subjected to an appropriate course of education one would obtain the adult brain.’’ Turing, A.M. (1950). Computing machinery and intelligence. Mind, 59, 433-460. 4

  10. What is machine learning? Definition by Tom Mitchell A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. Face Detection ◮ E : images (with bounding boxes) around faces ◮ T : given an image without boxes, put boxes around faces ◮ P : number of faces correctly identified 5

  11. Course Information Website www.cs.ox.ac.uk/people/varun.kanade/teaching/ML-AIMS-MT2017/ Lectures Mon-Fri: 10h30 - 12h30 (here) Practicals Mon-Fri: 14h-17h Demonstrators: Philip Lazos, David Martínez Textbooks Kevin Murphy - Machine Learning: A Probabilistic Perspective ◮ Online access through Bodleian library 6

  12. A few last remarks about this course As ML developed through various disciplines - CS, Stats, △ ! Neuroscience, Engineering, etc. , there is no consistent usage of notation or even names among the textbooks. At times you may find inconsistencies even within a single textbook. You will be required to read, both before and after the lectures. I will post suggested reading on the website. Resources: ◮ Wikipedia has many great articles about ML and background material ◮ Online videos: Andrew Ng on coursera, Nando de Freitas on youtube, etc. ◮ Many interesting blogs, podcasts, etc. 7

  13. NIPS Papers! Advances in Neural Information Processing Systems 1988 8

  14. NIPS Papers! Advances in Neural Information Processing Systems 1995 8

  15. NIPS Papers! Advances in Neural Information Processing Systems 2000 8

  16. NIPS Papers! Advances in Neural Information Processing Systems 2005 8

  17. NIPS Papers! Advances in Neural Information Processing Systems 2009 8

  18. NIPS Papers! Advances in Neural Information Processing Systems 2016 8

  19. NIPS Papers! Advances in Neural Information Processing Systems 2017 [video] 8

  20. Application: Boston Housing Dataset Numerical attributes Predict house cost ◮ Crime rate per capita ◮ Non-retail business fraction ◮ Nitric Oxide concentration ◮ Age of house ◮ Floor area ◮ Distance to city centre ◮ Number of rooms Categorical attributes ◮ On the Charles river? ◮ Index of highway access (1-5) Source: UCI repository 9

  21. Application: Object Detection and Localisation ◮ 200-basic level categories ◮ Here: Six pictures containing airplanes and people ◮ Dataset contains over 400,000 images ◮ Imagenet competition (2010-) ◮ All recent successes through very deep neural networks! 10

  22. Supervised Learning Training data has inputs x (numerical, categorical) as well as outputs y (target) Regression: When the output is real-valued, e.g., housing price Classification: Output is a category ◮ Binary classification: only two classes e.g., spam ◮ Multi-class classification: several classes e.g., object detection 11

  23. Unsupervised Learning : Genetic Data of European Populations Experience (E) Task (T) Performance (P) Source: Novembre et al. , Nature (2008) Dimensionality reduction - Map high-dimensional data to low dimensions Clustering - group together individuals with similar genomes 12

  24. Unsupervised Learning : Group Similar News Articles Group similar articles into categories such as politics, music, sport, etc. In the dataset, there are no labels for the articles 13

  25. Active and Semi-Supervised Learning Active Learning ◮ Initially all data is unlabelled ◮ Learning algorithm can ask a human to label some data Semi-supervised Learning ◮ Limited labelled data, lots of unlabelled data ◮ How to use the two together to improve learning? 14

  26. Collaborative Filtering : Recommender Systems Movie / User Alice Bob Charlie Dean Eve The Shawshank Redemption 7 9 9 5 2 The Godfather 3 ? 10 4 3 The Dark Knight 5 9 ? 6 ? Pulp Fiction ? 5 ? ? 10 Schindler’s List ? 6 ? 9 ? Netflix competition to predict user-ratings (2008-09) Any individual user will not have used most products Most products will have been use by some individual 15

  27. Reinforcement Learning ◮ Automatic flying helicopter; self-driving cars ◮ Cannot conceivably program by hand ◮ Uncertain (stochastic) environment ◮ Must take sequential decisions ◮ Can define reward functions ◮ Fun: Playing Atari breakout! [video] 16

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