ML

1. 3.1.1 Introduction to
Machine Learning
2nd
Edition
Knowledge Component 3: Acquiring Data and Knowledge
1
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
2

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
5

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 ?
6

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
7

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
8

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
10

11. Machine Learning
Area of Influence
Successful Applications
Forms of Machine Learning
Types of Learning Algorithms
Outline
11

12.  Learning to predict risk of failures for components
and systems of New York city power grid.
(Rudin et al. 2012 )
 Learning to analyze and predict the response of
wind turbine structures to varying wind field
characteristics. (Park et al. 2013)
 Learning to assess chlorine concentration in WDS.
(Cuesta Cordoba et al., 2014)
Successful Applications
12

13. Machine Learning
Area of Influence
Successful Applications
Forms of Machine Learning
Types of Learning Algorithms
Outline
13

14.  Supervised learning
A series of examples are used for feedback
 Unsupervised learning
No feedback
 Reinforcement learning
Indirect feedback after experience
Forms of Machine Learning
14

15. A learning task involves a set of input variables and a
set of output variables.
The set of possible relationships (hypotheses)
between input and output variables is known as the
hypothesis space.
Hypothesis have representations such as numerical
functions, symbolic rules, decision trees and artificial
neural nets.
Most learning is performed in a closed world where
the hypothesis space is predefined and finite.
Supervised Learning
15

16. The learning algorithm attempts to find the best
hypothesis that maps input to output using
“feedback”.
Feedback consists of a set of points (training data)
for which values of input and output variables are
known.
Since training data are used, this is supervised
learning.
Supervised Learning (cont'd.)
16

17. In unsupervised learning, output variables are not
known.
Unsupervised learning algorithms identify trends in
data and make inferences without knowledge of
correct answers.
Unsupervised Learning
17

18. Reinforcement learning is concerned with how
software ought to initiate actions in an environment
so as to maximize some notion of long-term reward.
Reinforcement learning algorithms identify ways to
maps states of the world to the actions the software
ought to take in those states.
Reinforcement learning may involve learning from
mistakes.
Reinforcement Learning
18

19. Machine Learning
Area of Influence
Successful Applications
Forms of Machine Learning
Types of Learning Algorithms
Outline
19

20. There are four types of machine learning algorithms:
 Rote
 Statistical
 Deductive
 "Exploration and discovery“
These types are another way to classify machine
learning and are mostly independent of the forms of
machine learning defined earlier. The next module
provides more detail.
Types of Learning Algorithms
20

21.  Give an example of a learning task that is easy for a
human being but hard for a computer.
 Name the different forms of machine learning.
 What is the difference between supervised and
unsupervised learning?
Review Quiz I

22.  Give an example of a learning task that is easy for a
human being but hard for a computer.
An example is image recognition. A 5-year old child is
able to distinguish between a car and a tree in a picture.
This task is difficult for a computer; it is hard to write a
program that distinguishes the two. A machine learning
algorithm could be used successfully to perform image
recognition
Answers to Review Quiz I

23.  Name the different forms of machine learning.
Supervised, unsupervised, reinforcement
 What is the difference between supervised and unsupervised
learning?
In case of supervised learning, there is a training set which
contains input and output for a number of examples. The
output is used as a feedback to learn from the data.
In unsupervised learning, there is no training set. This kind of
learning algorithms make inference from trends in data.
Answers to Review Quiz - I

24. 24
 Mitchell, T. Machine Learning. New York: McGraw-
Hill, 1997
 Kromanis et al.(2013). “Support vector regression
for anomaly detection from measurement
histories”.
 Dópido et al. (2013). ”Semisupervised self
learning for hyperspectral image classification”.
 Raphael, B. and Smith, I.F.C. “Engineering
informatics - fundamentals of computer-aided
engineering”, Wiley, 2013.
Further Reading

25. 25
 Cuesta Cordoba et al. (2014). “Using artificial neural
network models to assess water quality in water
distribution networks”.
 Park et al. (2013). “Multivariate analysis and
prediction of wind turbine response to varying wind
field characteristics based on machine learning”.
 Rudin et al.(1998). “Machine learning for the New
York city power grid”.
Further Reading