The learning method used by our approach is usually known in the artificial intelligence community as learning by observation, imitation learning, learning from demonstration, programming by demonstration, learning by watching or learning by showing. For consistency, learning by observation will be used from here on.

1. Nadar saraswathi college of arts &
science, theni.
Department of cs & it
ARTIFICIAL INTELLIGENCE
PRESENTED BY
G.KAVIYA
M.SC(IT)
TOPIC:LEARNING FROM
OBSERVATION

2. LEARNING
LEARNING FROM OBSERVATION
 Forms of learning
 Ensemble learning
 Computational learning theory

3. LEARNING:
Learning is Agent’s percepts should be used for
acting.
It also used for improving the agents ability to act
in the future.
Learning takes places as the agents observes, its
interactions with the world and its own decision making
processes.

4. FORMS OF LEARNING:
Learning Agent can be thought of as containing
a Performance Element, that decides, what actions to take,
and a Learning Elements that modifies the performance
elements to take better decisions.
Three major issues in learning element design
 Which components the performance element are to be
learned.
 What feedback is available to learn these components.
 What representation is used for the components.

5. Components of Agents are;
 A direct mapping from conditions on
the current state to actions.
 A means to infer relevant properties of
the world from the percept sequence.
 Information about the way the world
evolves and about the results of the
possible action the agent can take.
 Utility information indicating the
desirability of world states.
 Action value information indicating the
desirability of action.
 Goals the describe classes of the state
whose achievement maximizes the
agent’s utility.

6. Classified into three categories:
Supervised Learning.
Unsupervised Learning.
Reinforcement Learning.

7. Supervised Learning:
The Learning here is performed with the
help of teacher. Let us take the example of the learning
process of the small child.
The child doesn’t know how to read/write.
He/she is being taught by the parents at home and by the
teacher in school.
The children are recognize the alphabet,
numerals, etc. Their and every action is supervised by a
teacher.

8. Continue;
Actually, a child works on
the basis of the output that
he/she has to produce. All
these real-time events
involve supervised learning
methodology.
Similarly, in ANNs
following the supervised
learning, each input vector
requires a corresponding
target vector, which
represents the desired
outputs.
The input vector
along with the target vector
is called training pair.

9. Continue;
 In this type of training, a
supervisor or teacher is
required for error
minimization. Hence, the
network trained by this
method is said to be using
supervised training
methodology.
 In supervised learning, It is
assumed that the correct
“target” output values are
known for each input pattern.
 The input vector is
presented to the network,
Which result is an output
vector. The output vector is the
actual output vector. Then the
actual output vector is
compared with the desired
output vector.
 If there exists a difference
between the two output
vectors then an error signal is
generated by the network. This
error signal is used for
adjustment of weights until the
actual output matches the
desired output.

10. Un Supervised Learning:
 The learning here is
performed without the help of
teacher. Consider, learning
process of a tadpole, it learns
by itself, that is, a child fish
learns to swim by itself, it is not
taught by its mother.
 Thus, Its learning process
is independent and is not
supervised by a teacher.
 In ANNs following
unsupervised learning, the
input vectors of similar type
are grouped without the use of.

11. Reinforcement Learning:
 The learning process is
similar to supervised learning.
In the case of supervised
learning the correct target
output values are known for
each input pattern.
 But, In some cases, Less
information might be available.
For example, the network
might be told that its actual
output is only “50% correct “or
so. Thus, Here only critic
information is available, not the
exact information.
 The learning information
based on the critic information
is called reinforcement learning
and the feedback sent is called
reinforcement signal.

12. ENSEMBLE OF LEARNING:
 Learn multiple
alternative definitions of
a concept using different
training data or different
learning algorithms.
 Combine decisions
of multiple definitions,
eg. Using weighted
voting.

13. VALUE OF ENSEMBLES
When combing multiple independent and diverse
decisions each of which is at least more accurate than random
guessing, random errors cancel each other out, correct decisions
are reinforcement.
Generate a group of base-learners which when
combined has higher accuracy.
Different learners use different;
Algorithm.
Hyperparameters.
Representations/Modalities/Views.
Training sets.
Subproblems.

14. Ensembles:

15. BOOSTING:
Also uses voting/averaging but models are
weighted according to their performance.
Iterative procedure: new models are influenced
by performance of previously built ones.
* New model is encouraged to become
expert for instances classified incorrectly by earlier
models.
* Intuitive justification: models should be
experts that complement each other.
There are several variants of this algorithm.

16. Continue;
 STRONG LEARNER:
Objective of machine learning.
o Take labeled data for training.
o Produce a classifier which can be
arbitrarily accurate.
o Strong learners are very difficult to
construct.
 WEAKER LEARNER:
o Take labeled data for training.
o Produce a classifier which is more
accurate than random guessing.
o Constructing weaker learners is
relatively easy.

17. ADAPTIVE BOOSTING:
Each rectangle corresponds to an example, with
weight proportional to its height.
Crosses corresponds to misclassified examples.
Size of decision tree indicates the weight of that
classifier in the final ensemble.
Using Different Data Distribution
* Start with uniform weighting.
* During each step of learning.
Increase weights of the examples which are not
correctly learned by the weak learner.
Decrease weights of the examples which are
correctly learned by the weak learner.

18. Continue;
IDEA:
focus on difficult example which
are not correctly classified in the
previous steps
WEIGHTED VOTING:
construct strong classifier by
weighted voting of the weak classifier.
IDEA:
Better weak classifier gets a
larger weight.
Iteratively add weak classifiers
Increase accuracy of the
combined classifier through
minimization of a cost function.

19. COMPUTATIONAL LEARNING
THEORY
 Computational learning theory characterize. The difficulty of
several types of machine learning problem.
 Capabilities of several types of ML algorithm.
 CLT seeks answer, question such as;
a) “under what conditions is successful learning possible and
impossible?”
b) “under what conditions is a particular learning algorithm
assured of learning successfully?” it means that, what kind of
task are learnable, what kind of data is required for
learnability.

20. Various issues are:
Sample complexity:
How many training examples are needed for a
learner to converge (with high probability) to a successful
hypothesis?
Computational complexity:
How much computational effort is needed for a
learner to converge to a successful hypothesis?
Mistake bound:
How many training examples will the learner
misclassify before converging to a successful hypothesis?

21. (PAC) Probably Learning an Approximately
Correct hypothesis:-
A particular setting for the learning problem,
called the probably approximately correct(PAC) learning
model.
This model of learning is based on following
points:
1. Specifying problem setting that defines PAC
model.
2. How many training examples are required.
3. How much computational are required in order
to learn various classes of target functions within PAC

22. Problem setting:-
X : Set of all the instance (eg: set of people)
each described by attributes <age, height>.
C : Target concept the leaner need to learn.
C: X{0,1}
L : Learner have to learn “people who are
skiers”
C(x) =1 : positive training example
C(x) =0 : negative training example
Error of a hypothesis: True error, denoted by errorD
(h), of hypothesis h w.r.t target concept c and
distribution D is probability that h will misclassify an
instance drawn at random according to D.
errorD (h) = pr [c(x) not equal to h(x)]
XED

23. PAC Learnability:
No of training examples needed to learn a
hypothesis h, for which
errorD (h) = 0.
For these two difficulties, following measures can be
taken:-
1. No requirement of zero error hypothesis for learner L. So
a bound to error can be set by constant E, that can be made small.
2.Not necessary that learner succeed for every sequence of
randomly drawn training example. So learner probably learn a
hypothesis that is approximately correct.
Bounded by same constant S which is,

24. Definition:
Consider a concept class define over a set of instance
X of kngtn n and a learner L using hypothesis space H.
C is PAC learnable by L using H if for all CEC.
distribution D over X
E such that 0<E<1/2 and
S such that 0<S<1/2
Learner L will with Probably at least (1-8)
output a hypothesis nEH such that
errorD (n) <= E.