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									    INTRODUCTION TO
                 Slide 2
•   Knowledge representation
•   Definition of machine learning
•   Sample digitizing
•   Learning types
•   Learning paradigma
•   Learning strategies
     Knowledge Representation
   In this part we will mainly focus on
    digitizing the information

   All the events in the world are needed to
    be described as some digital or analog
For the currency prediction:

The Inputs:
I1: The currency value for this term
I2: Inflation rate
I3: Consumer index
I4: Stock index
I5: Periodically exportation inrease/decrease

The Output: O: The currency value for the next
Sexuality: For male “1” otherwise “0”.
If the wife or husband works “1” otherwise “0”
Job: Teacher: 0, Professor: 1, In the bank: 2, Engineer: 3,
Marketting: 4, doctor 5
If he or she has his own house “1” otherwise 0”
If he has a car “1” otherwise “0”
If he has a problem about his payments “1” otherwise “0”
FINAL DECISION: Positive “1” Negative (riscy) “0”
          Machine learning:
    The machine learning field evolved
    from the broad field of artificial
    intelligence, which aims to mimic
    intelligent abilities to humans by
             Learning types
 Learning   from the habits
 Learning   from the views
 Learning   from the commands
 Learning   from the samples
 Learning   by the way of analogy
 Learning   from the explanations
 Learning   from the experiments
 Learning   from the discoveries
In general, the machine learning systems are
designed for 2 purposes:

1- Hetereo-association: By using all learning
  types mentioned above, trying to describe
  the sistem generally based on learning. All
  samples     are   evaluated   and     some
  generalizations are obtained about the
  problem especially in classification and
  prediction problem
2- Auto-association: In this case one
 event is learned and the data, the
 system had, is used to characterise
 the problem. In this case the system
 can describe the missing      points.
 Human face recognition is an
 example for this. The system can
 also succeed if there is problem on
 the image.
         Learning Paradigmas:
 Some paradigms and approaches are
 improved to convert the studies
 based on learning to machine
 learning schemes. Some samples are
 given below:
1- Symbolic procesing
2- Connectionist systems
3- Statistical pattern recognition
4- Genetic algorithms and evolutionary
5- Case based learning
       The Learning Strategies:
1.   Supervised learning: A teacher is
     needed here. The teacher should give to
     the system input and output data for the
     problem to be learned.i.e. Multilayer neural
2.   Reinforcement learning: Here the
     teacher just helps to the system. But,
     instead of showing all outputs for the
     inputs set, the teacher expects the system
     to produce the output for the given inputs,
     and produces a signal about the output is
     true or false. Based on this signal, the
     system goes on learning process. i.e. LVQ
3- Unsupervised learning: There is
  not teacher here helping to the learning of
  the system. Here only the input values are
  shown to the system, and the system is
  wanted to learn the relationships among
  the inputs by itself. This is mostly used for
  classification problems. However, after the
  learning process, the labelling has to be
  done by the user. ie. ART networks are
  using this strategy when it is learning.
4- Hybrit strategies: Any learning
 strategy, which uses two or more of
 these 3 learning strategies. Here, the
 partially supervised or partially
 unsupervised learning systems are
 described.   Radial    bases    neural
 networks or probabilistic neural
 networks can be given as examples.
        Online and Offline Learning
On-line learning: The system can work
 as a real time system. When the system is
 working, the learning process can go on.
 The rule in ART network and Kohonen
 learning rule are from this learning
Off-line learning: These systems are
 trained off-line and then used. For any
 new data the system is trained off-line.
 i.e. Delta learning rule in neural networks.

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