4. Quick calibration:
Who has heard of Machine Learning?
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5. Quick calibration:
Who has heard of Machine Learning?
Who has used Machine Learning?
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6. Quick calibration:
Who has heard of Machine Learning?
Who has used Machine Learning?
Who has built new Machine Learning tools?
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7. PROBLEM:
DATA
ACTIONABLE KNOWLEDGE
That’s roughly the problem Machine Learning addresses
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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?
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9. Knowledge is not concrete
“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
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10. ?
We have data But we want abstractions
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11. What is an abstraction
anyway?
• Anything whose description does not
depend exclusively on the bits you have
• Notion of generalisation is fundamental
• Abstraction always involves assumptions
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12. Ready to define Machine Learning:
• Machine Learning is the science of
automating the process of abstraction from
raw data and assumptions
Raw Data
Machine Learning Abstraction
Assumptions
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13. Data: (painted image) + (dataset of normal images)
+
Assumption: the non-painted parts of the painted image
behave as the images in the dataset
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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
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15. Several forms of abstraction
Cluster data
Classify data
Predict from data
Summarise data
Decide based on data
etc...
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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
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24. What Machine Learning IS NOT
Find 01001000:
Machine Learning is not exact pattern matching
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25. What Machine Learning IS NOT
Find 01001000:
Machine Learning is not exact pattern matching
This is “just” classical computer science
classical “database query”, deduction
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26. What Machine Learning IS NOT
Find 01001000:
Machine Learning is not exact pattern matching
This is “just” classical computer science
classical “database query”, deduction
Machine Learning involves induction
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27. But Machine Learning IS NOT classical statistics either
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28. But Machine Learning IS 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)
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31. Big Data and Machine 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
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32. Machine Learning and Data 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)
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33. When is Machine Learning 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 Machine Learning 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!!
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35. http://tiberiocaetano.com
http://www.nicta.com.au/research/machine_learning
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