Machine Learning

1. 3.1.1 Introduction to
Machine Learning
2nd
Edition
Knowledge Component 3: Acquiring Data and Knowledge
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Ian F. C. Smith
EPFL, Switzerland

2. Module Information
• Intended audience
– Novice
• Key words
– Machine learning
– Supervised learning
– Unsupervised learning
• Reviewer (1st
Edition)
– Ian Flood, U of Florida,
Gainesville, USA
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3. 3
What there is to learn
At the end of this module, there will be answers to the
following questions (see the quiz):
•What are the different ways in which computers can
learn?
•Are there learning tasks that humans can do much
better than computers?

4. Machine Learning
Area of Influence
Successful Applications
Forms of Machine Learning
Types of Learning Algorithms
Outline
4

5. Humans learn from experience and adapt their actions
for future tasks.
Can machines adapt their behavior using
experience?
Since the 1950s, researchers have been trying to
develop techniques that enable machines to learn.
There have been much success in areas such as
automatic control, recognition systems and natural
language processing. Other successes are emerging.
Machine Learning
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6. An algorithm is said to learn from experience E with
respect some class of tasks T and performance
measure P …
… if its performance at tasks in T, as measured by
P, improves as it does task in T (experience E).
What is Machine Learning ?
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7. Example 1 Learning to recognize faces
– T: recognize faces
– P: % of correct recognitions
– E: opportunity to makes guesses and
being told what the truth is
Example 2 Learning to find clusters in data
– T: finding clusters
– P: compactness of groups detected
– E: analyses of a growing set of data
Machine Learning - Examples
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8. Existing machine learning techniques are applicable
only when the learning task is well-defined.
In many engineering applications, it is possible to
formalize the learning task of specific “sub-
problems”.
Current Status
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9. Machine Learning
Area of Influence
Successful Applications
Forms of Machine Learning
Types of Learning Algorithms
Outline
9

10. Machine learning research is often interdisciplinary.
There are synergies in the following fields:
 Statistics
 Brain models
 Adaptive Control Theory
 Psychology
 Artificial Intelligence
 Evolutionary models
 Information theory
 Philosophy
Areas of influence
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