1. Introduction to Machine
Learning
Lecture 2
Albert Orriols i Puig
aorriols@salle.url.edu
i l @ ll ld
Artificial Intelligence – Machine Learning
Enginyeria i Arquitectura La Salle
gy q
Universitat Ramon Llull

2. Recap of Lecture 1
Knowledge
Kno ledge
Search
representation
We have seen several search techniques:
Blind search, heuristic search, adversary search … GAs
We have seen several ways of representing our
knowledge
Logic-based representation, rule-based representation …
g p p
We have discussed reasoning mechanisms to deal with
uncertainty, incompleteness and inconsistency
y p y
We set the basis. But, the most interesting is still missing
Machine learning
M hi l i
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Artificial Intelligence Machine Learning

3. Today’s Agenda
What’s Machine Learning
Why Machine Learning?
Where is ML Headed and Which Are our
Goals?
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Artificial Intelligence Machine Learning

4. What’s Machine Learning
Build computer programs that automatically improve
p pg yp
with experience
Can you be more precise? (Mitchell 1997)
(Mitchell,
Learning = Improving with experience at some task
Improve over task T
I tk
With respect to a performance measure P
Based on experience E
B d i
E.g.: Learn to play checkers
T: Play checkers
P: % of games won in world tournament
E: opportunity to play against self
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Artificial Intelligence Machine Learning

5. What Does this Involve?
Represent the knowledge
p g
Logic-based representation
Rule-based representation
Rl b d t ti
Frame-based representation
…
Search toward better solutions
Blind search …, but not really efficient!
Non-systematic techniques: G
GAs, etc.
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Artificial Intelligence Machine Learning

6. Why Machine Learning?
Several factors affected the increasing appeal of ML
From the machines point of view:
Recent progress in algorithms and theory
Computational power is available
From the industry point of view:
Growing flood of online data
GB hours of data:
Remote sensors, telescopes scanning the skies, scientific
simulations…
Budding industry
Machine learning may help scientists, businessmen, and
engineers
Classify and segment data
y g
Formulate hypotheses
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Artificial Intelligence Machine Learning

7. Why Machine Learning?
There are three special niches for ML:
p
Data mining: extract information from historical data to help
dec s o
decision making
ag
Medical records Extract knowledge to help doctors
Software applications that are too complex to build a hard-
wired solution for
Autonomous driving
g
Speech recognition
Self customizing programs
Recommender systems (RS)
New generation RS
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Artificial Intelligence Machine Learning

8. What’s Data Mining in a Picture
1
J. Han, M. Kamber.
J Han M Kamber Data Mining Concepts and
Mining.
Techniques. Morgan Kaufmann, 2006(Second Edition)
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Artificial Intelligence Machine Learning

9. Do You Have a Definition for DM
1
Many definitions of data mining. A specially interesting
y g p y g
one is provided by Duda, Hart, & Stork (2002)
Data mining is the process of extracting interesting useful and
interesting, useful,
novel information from data
Many other definitions, but for sure, data mining is not
Look up an entry in a data base
Query a web search engine
How this relates to ML?
ML provides methods to dig these data
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Artificial Intelligence Machine Learning

10. Example of DM
1
Ge
Given
9714 patient records, each one describing pregnancy and birth
Each patient record consists of 215 features
Learn to predict
Classes of future patients at risk for Emergency Cesarean Section
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Artificial Intelligence Machine Learning

11. Example of DM
1
O e of t e u es ea ed
One o the rules learned:
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Artificial Intelligence Machine Learning

12. Example 2 of DM
1
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Artificial Intelligence Machine Learning

13. Example 3 of DM
1
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Artificial Intelligence Machine Learning

14. Example 4 of DM
1
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Artificial Intelligence Machine Learning

15. Other Examples of DM
1
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Artificial Intelligence Machine Learning

16. 2 Problems Too Difficult to Program by Hand
Autonomous Land Vehicle in a Neural Network (ALVINN)
( )
drives 70 mph on highways
Perception system which learns to control the NAVLAB
vehicles by watching a person drive
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Artificial Intelligence Machine Learning

17. Self-Customizing Software
3
Originally at www.wisewire.com
System that delivered a unique
blend of AI with collaborative and
content-based filtering
Purchased by Lycos, Inc in 1998
Integrated in Lycos products
Documents search for and find
interested people.
No longer available at
www.wisewire.com
Visit the f ll i
Vi it th following webpage for
b f
more information:
http://www.cse.iitb.ac.in/dbms/Data
http://www cse iitb ac in/dbms/Data
/Papers-Other/Web/wisewire.html
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Artificial Intelligence Machine Learning

18. Where is All this Headed?
Today:
y
First-generation systems are evolving toward competent systems
that ca tackle so e important p ob e s efficiently and scalably
a can ac e some po a problems e c e t y a d sca ab y
Give me some prove of that
Ask Google Yahoo Docomo Labs …
Google, Yahoo,
Tomorrow
T
Semantic networks integrated in DM systems
Can you image face book mining?
DM in many decision processes: marketing, industry, science …
DM as individual recommender systems
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Artificial Intelligence Machine Learning

19. But… Slow Down!
Where are we?
We are still beginning!
What’s thi
Wh t’ this course about?
b t?
Starting in ML, understanding the problems that we can solve
now and the f
d h future problems
bl
This course is not
a typical ML course in which we will go through different
paradigms
Engineers solve problems, so this course tries to follow
this idea by
y
describing important challenges
presenting one or several of the most influential techniques to
address this challenge
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Artificial Intelligence Machine Learning

20. Next Class
Characteristics Desired for ML Methods
Summary of the Paradigms that We Won’t
Won t
Study
Summary of the Problems that We Will Study
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Artificial Intelligence Machine Learning

21. Introduction to Machine
Learning
Lecture 2
Albert Orriols i Puig
aorriols@salle.url.edu
i l @ ll ld
Artificial Intelligence – Machine Learning
Enginyeria i Arquitectura La Salle
gy q
Universitat Ramon Llull