![(e) Ground Truth
Clustering
(i) Ground Truth
Figure 2: Resulting motion
based algorithms. 2(a)-2(d)
[10] S. M. Goldfeld and R
http://home.dei.polimi.it/matteucc/
Holland Publishing C
Clustering/tutorial_html/
[11] D. W. Hosmer. Maxim
lines. In Communicati
Friday, 24 February 2012](https://image.slidesharecdn.com/introductiontomachinelearning-120403212953-phpapp01/85/Introduction-to-Machine-Learning-16-320.jpg)
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Introduction to Machine Learning
The document introduces machine learning as the science of automating abstraction from raw data through generalization and assumptions. It contrasts machine learning with classical methods such as exact pattern matching and classical statistics, highlighting its complexity and reliance on numerical solutions. Additionally, it discusses when machine learning is beneficial, particularly when seeking actionable knowledge from data without explicit, bit-level descriptions.
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Introduction to Machine Learning
- 1. Introduction to Machine Learning Tiberio Caetano NICTA and Australian National University Friday, 24 February 2012
- 2.
- 3. Quick calibration: Friday, 24February 2012
- 4. Quick calibration: Who has heard of Machine Learning? Friday, 24 February 2012
- 5. Quick calibration: Who has heard of Machine Learning? Who has used Machine Learning? Friday, 24 February 2012
- 6. Quick calibration: Who has heard of Machine Learning? Who has used Machine Learning? Who has built new Machine Learning tools? Friday, 24 February 2012
- 7. PROBLEM: DATA ACTIONABLE KNOWLEDGE That’s roughly the problem Machine Learning addresses Friday, 24 February 2012
- 8. BLUE: DATA RED: KNOWLEDGE - Is this email spam or not spam? - Is there a face in this picture? - Should I lend money to this customer given his spending behaviour? Friday, 24 February 2012
- 9. Knowledge is notconcrete “Face” is an abstraction “Spam” is an abstraction “Who to lend to” is an abstraction You don’t find faces, spam or financial advice in datasets you just find bits Friday, 24 February 2012
- 10. ? We have data But we want abstractions Friday, 24 February 2012
- 11. What is anabstraction anyway? • Anything whose description does not depend exclusively on the bits you have • Notion of generalisation is fundamental • Abstraction always involves assumptions Friday, 24 February 2012
- 12. Ready to defineMachine Learning: • Machine Learning is the science of automating the process of abstraction from raw data and assumptions Raw Data Machine Learning Abstraction Assumptions Friday, 24 February 2012
- 13. Data: (painted image)+ (dataset of normal images) + Assumption: the non-painted parts of the painted image behave as the images in the dataset Friday, 24 February 2012
- 14. Data: (painted image)+ (dataset of normal images) + Assumption: the non-painted parts of the painted image behave as the images in the dataset Abstraction: corrected image Friday, 24 February 2012
- 15. Several forms ofabstraction Cluster data Classify data Predict from data Summarise data Decide based on data etc... Friday, 24 February 2012
- 16. (e) Ground Truth Clustering (i) Ground Truth Figure 2: Resulting motion based algorithms. 2(a)-2(d) [10] S. M. Goldfeld and R http://home.dei.polimi.it/matteucc/ Holland Publishing C Clustering/tutorial_html/ [11] D. W. Hosmer. Maxim lines. In Communicati Friday, 24 February 2012
- 17. Dimensionality Reduction andVisualization http://isomap.stanford.edu/datasets.html Friday, 24 February 2012
- 18.
- 19. Classification {spam; not spam} {0,1,2,3,4,5,6,7,8,9} Friday, 24 February 2012
- 20. Structured Prediction Image Understanding Protein Structure Prediction Machine Translation Image credit: S. Gould Friday, 24 February 2012
- 21. Structured Prediction Chess, NY, Kasparov, WTC Kangaroo, Sun, Sea, Australia Friday, 24 February 2012
- 22. What Machine LearningIS NOT Friday, 24 February 2012
- 23. What Machine LearningIS NOT Find 01001000: Friday, 24 February 2012
- 24. What Machine LearningIS NOT Find 01001000: Machine Learning is not exact pattern matching Friday, 24 February 2012
- 25. What Machine LearningIS NOT Find 01001000: Machine Learning is not exact pattern matching This is “just” classical computer science classical “database query”, deduction Friday, 24 February 2012
- 26. What Machine LearningIS NOT Find 01001000: Machine Learning is not exact pattern matching This is “just” classical computer science classical “database query”, deduction Machine Learning involves induction Friday, 24 February 2012
- 27. But Machine LearningIS NOT classical statistics either Friday, 24 February 2012
- 28. But Machine LearningIS NOT classical statistics either - Complex rather than simple models (forget Gaussianity, forget linearity) - Numerical rather than analytical solution (forget pencil-and-paper: need hardcore numerical optimization) - VERY High rather than low dimensional (p>>n rather than n>>p) Friday, 24 February 2012
- 29. Some popular technologiesdriven by Machine Learning Recommender Systems Friday, 24 February 2012
- 30. Some popular technologiesdriven by Machine Learning Social media Friday, 24 February 2012
- 31. Big Data andMachine Learning Parallelism is crucial - Linear algebraic approaches favoured (matrix multiplication-based) - Much of Feature Extraction can be parallelised - Model Training is another story: usually needs syncing Friday, 24 February 2012
- 32. Machine Learning andData Mining Data Mining is a buzzword and in that sense it includes Machine Learning In a more strict sense, Data Mining is often associated to data analysis without necessarily doing predictive analytics (which is the hallmark of Machine Learning) Friday, 24 February 2012
- 33. When is MachineLearning helpful? DATA ACTIONABLE KNOWLEDGE When you don’t really know how to find an explicit (at the bit-level) description for your abstraction or “actionable knowledge” Friday, 24 February 2012
- 34. When is MachineLearning helpful? DATA ACTIONABLE KNOWLEDGE When you don’t really know how to find an explicit (at the bit-level) description for your abstraction or “actionable knowledge” And this is common!! Friday, 24 February 2012
- 35. http://tiberiocaetano.com http://www.nicta.com.au/research/machine_learning Friday, 24 February 2012