Learning, Types of Learning: Machine Learning Techniques

Que 1.1. Define the term learning. What are the components of a
learning system ?

Learning refers to the change in a subject’s behaviour to a given situation
brought by repeated experiences in that situation, provided that the
behaviour changes cannot be explained on the basis of native response
tendencies, matriculation or temporary states of the subject.
Learning agent can be thought of as containing a performance element
that decides what actions to take and a learning element that modifies
the performance element so that it makes better decisions.
The design of a learning element is affected by three major issues :
a. Components of the performance element.
b. Feedback of components.
c. Representation of the components.
Acquisition of new knowledge :
a. One component of learning is the acquisition of new knowledge.
Machine Learning Techniques 1–3 L (CS/IT-Sem-5)
b. Simple data acquisition is easy for computers, even though it is
difficult for people.
Problem solving :
The other component of learning is the problem solving that is required
for both to integrate into the system, new knowledge that is presented
to it and to deduce new information when required facts are not been
presented.

Que 1.2. Write down the performance measures for learning.

Following are the performance measures for learning are :

Generality :
a. The most important performance measure for learning methods is
the generality or scope of the method.
b. Generality is a measure of the case with which the method can be
adapted to different domains of application.
c. A completely general algorithm is one which is a fixed or self adjusting
configuration that can learn or adapt in any environment or
application domain.
Efficiency :
a. The efficiency of a method is a measure of the average time required
to construct the target knowledge structures from some specified
initial structures.
b. Since this measure is often difficult to determine and is meaningless
without some standard comparison time, a relative efficiency index
can be used instead.
Robustness :
a. Robustness is the ability of a learning system to function with
unreliable feedback and with a variety of training examples, including
noisy ones.
b. A robust system must be able to build tentative structures which
are subjected to modification or withdrawal if later found to be
inconsistent with statistically sound structures.
Efficacy :
a. The efficacy of a system is a measure of the overall power of the
system. It is a combination of the factors generality, efficiency, and
robustness.
Ease of implementation :
a. Ease of implementation relates to the complexity of the programs
and data structures, and the resources required to develop the
given learning system.

Introduction 1–4 L (CS/IT-Sem-5)
b. Lacking good complexity metrics, this measure will often be
somewhat subjective.

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Que 1.3. Discuss supervised and unsupervised learning.

Supervised learning :

Supervised learning is also known as associative learning, in which
the network is trained by providing it with input and matching
output patterns.
Supervised training requires the pairing of each input vector with
a target vector representing the desired output.
The input vector together with the corresponding target vector is
called training pair.
During the training session an input vector is applied to the network,
and it results in an output vector.
This response is compared with the target response.
If the actual response differs from the target response, the network
will generate an error signal.
This error signal is then used to calculate the adjustment that
should be made in the synaptic weights so that the actual output
matches the target output.
The error minimization in this kind of training requires a supervisor
or teacher.
These input-output pairs can be provided by an external teacher, or
by the system which contains the neural network (self-supervised).
Supervised training methods are used to perform non-linear
mapping in pattern classification networks, pattern association
networks and multilayer neural networks.
Supervised learning generates a global model that maps input objects
to desired outputs.
In some cases, the map is implemented as a set of local models such
as in case-based reasoning or the nearest neighbour algorithm.
In order to solve problem of supervised learning following steps are
considered :
i. Determine the type of training examples.
ii. Gathering a training set.
iii. Determine the input feature representation of the learned
function.
iv. Determine the structure of the learned function and
corresponding learning algorithm.
v. Complete the design.
Unsupervised learning :
It is a learning in which an output unit is trained to respond to
clusters of pattern within the input.
Unsupervised training is employed in self-organizing neural
networks.
This training does not require a teacher.
In this method of training, the input vectors of similar types are
grouped without the use of training data to specify how a typical
member of each group looks or to which group a member belongs.
During training the neural network receives input patterns and
organizes these patterns into categories.
When new input pattern is applied, the neural network provides an
output response indicating the class to which the input pattern
belongs.
If a class cannot be found for the input pattern, a new class is
generated.
Though unsupervised training does not require a teacher, it requires
certain guidelines to form groups.
Grouping can be done based on color, shape and any other property
of the object.
It is a method of machine learning where a model is fit to
observations.
It is distinguished from supervised learning by the fact that there is
no priori output.
In this, a data set of input objects is gathered.
It treats input objects as a set of random variables. It can be used in
conjunction with Bayesian inference to produce conditional
probabilities.
In unsupervised learning, system is supposed to discover statistically
salient features of the input population.
Unlike the supervised learning paradigm, there is not a priori set of
categories into which the patterns are to be classified; rather the
system must develop its own representation of the input stimuli.

Que 1.4. Describe briefly reinforcement learning ?

Reinforcement learning is the study of how artificial system can learn to
optimize their behaviour in the face of rewards and punishments.
Reinforcement learning algorithms have been developed that are closely
related to methods of dynamic programming which is a general approach
to optimal control.
Reinforcement learning phenomena have been observed in psychological
studies of animal behaviour, and in neurobiological investigations of
neuromodulation and addiction.
The task of reinforcement learning is to use observed rewards to learn
an optimal policy for the environment.
An optimal policy is a policy that maximizes the expected total reward.
Without some feedback about what is good and what is bad, the agent
will have no grounds for deciding which move to make.
The agents need to know that something good has happened when it
wins and that something bad has happened when it loses.
This kind of feedback is called a reward or reinforcement.
Reinforcement learning is very valuable in the field of robotics, where
the tasks to be performed are frequently complex enough to defy
encoding as programs and no training data is available.
The robot’s task consists of finding out, through trial and error (or
success), which actions are good in a certain situation and which are
not.
In many cases humans learn in a very similar way.
For example, when a child learns to walk, this usually happens without
instruction, rather simply through reinforcement.
Successful attempts at working are rewarded by forward progress, and
unsuccessful attempts are penalized by often painful falls.
Positive and negative reinforcement are also important factors in
successful learning in school and in many sports.
In many complex domains, reinforcement learning is the only feasible
way to train a program to perform at high levels.

Que 1.5. What are the steps used to design a learning system ?

Steps used to design a learning system are :

Specify the learning task.
Choose a suitable set of training data to serve as the training experience.
Divide the training data into groups or classes and label accordingly.
Determine the type of knowledge representation to be learned from the
training experience.
Choose a learner classifier that can generate general hypotheses from
the training data.
Apply the learner classifier to test data.
Compare the performance of the system with that of an expert human.

Que 1.6. Write short note on well defined learning problem with
example.

Well defined learning problem :
A computer program is said to learn from experience E with respect to some
class of tasks T and performance measure P, if its performance at tasks in T,
as measured by P, improves with experience E.
Three features in learning problems :

The class of tasks (T)
The measure of performance to be improved (P)
The source of experience (E)
For example :
A checkers learning problem :
a. Task (T) : Playing checkers.
b. Performance measure (P) : Percent of games won against
opponents.
c. Training experience (E) : Playing practice games against itself.
A handwriting recognition learning problem :
a. Task (T) : Recognizing and classifying handwritten words within
images.
b. Performance measure (P) : Percent of words correctly classified.
c. Training experience (E) : A database of handwritten words with
given classifications.
A robot driving learning problem :
a. Task (T) : Driving on public four-lane highways using vision sensors.
b. Performance measure (P) : Average distance travelled before an
error (as judged by human overseer).
c. Training experience (E) : A sequence of images and steering
commands recorded while observing a human driver.

Que 1.7. Describe well defined learning problems role’s in
machine learning.

Well defined learning problems role’s in machine learning :

Learning to recognize spoken words :
a. Successful speech recognition systems employ machine learning in
some form.
b. For example, the SPHINX system learns speaker-specific strategies
for recognizing the primitive sounds (phonemes) and words from
the observed speech signal.
c. Neural network learning methods and methods for learning hidden
Markov models are effective for automatically customizing to
individual speakers, vocabularies, microphone characteristics,
background noise, etc.
Learning to drive an autonomous vehicle :
a. Machine learning methods have been used to train computer
controlled vehicles to steer correctly when driving on a variety of
road types.
b. For example, the ALYINN system has used its learned strategies to
drive unassisted at 70 miles per hour for 90 miles on public highways
among other cars.
Learning to classify new astronomical structures :
a. Machine learning methods have been applied to a variety of large
databases to learn general regularities implicit in the data.
b. For example, decision tree learning algorithms have been used by NASA to learn how to classify celestial objects from the second
Palomar Observatory Sky Survey.
c. This system is used to automatically classify all objects in the Sky
Survey, which consists of three terabytes of image data.
Learning to play world class backgammon :
a. The most successful computer programs for playing games such as
backgammon are based on machine learning algorithms.
b. For example, the world’s top computer program for backgammon,
TD-GAMMON learned its strategy by playing over one million
practice games against itself.

Que 1.1. Define the term learning. What are the components of alearning system ?