prof. k. rajan
Post on 06-Apr-2018
223 Views
Preview:
TRANSCRIPT
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 1/65
1/29/2012 1
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 2/65
K.RajanDepartment
of
Electrical
&
Electronics
Engineering
u a o y ec n c o ege
Annamalainagar
õí è¢ñ¢
Department of Electrical & Electronics Engineering
Muthiah Polytechnic College, Annamalainagar1/29/2012 2
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 3/65
Learning
Learning is a general term that denotes the way in which peopleand machine enrich their knowledge and improve their skills.
Learning is one of the component of Intelligence.
Department of Electrical & Electronics Engineering
Muthiah Polytechnic College, Annamalainagar1/29/2012 3
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 4/65
Learning Algorithm
Learning algorithm is a procedure to learn systematically.
Al orithm s ecifies how to learn or ac uire the knowled e.
Department of Electrical & Electronics Engineering
Muthiah Polytechnic College, Annamalainagar1/29/2012 4
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 5/65
Artificial Intelligence (AI)
AI is a branch of Computer Science that deals with thestudy and creation of computer systems that exhibit
some form of intelligence.
,
and use knowledge in a meaningful way.
The term Artificial Intelligence was coined by John
McCarthy, in 1956 at MIT
Department of Electrical & Electronics Engineering
Muthiah Polytechnic College, Annamalainagar1/29/2012 5
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 6/65
Components of Intelligence
Learning – It is the process of acquiring knowledge, skills, experience by study
and training.
Reasoning –It refers to the ability of drawing conclusions that are appropriate
to the
situation
in
hand.
Understanding – It refers to the identification of the significance,
interpretation, or explanation for certain data or information. It is the ability to
.
Creativity‐ It is the ability to generate new ideas or to conceive new perspectives
on existing data.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 6
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 7/65
Domain areas of AI
ame p ay ng earc
Speech recognition
Computer Vision
Expert systems
Heuristic classification
Robotics
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 7
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 8/65
Knowledge based systems
now e ge s e ne as e remem er ng o prev ous y earne ma er a . s
the result
of
learning
and
reasoning.
,
cells) in the brain, which contains approximately 10 12 neurons.
Knowled e la s an im ortant role in buildin intelli ent knowled e based
systems.
Knowledge needs to be acquired from different sources such as procedures,
rules and
facts.
Knowledge is to be stored/organised in such a way that retrieval of information
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 8
can be easy.
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 9/65
Knowledge based systems
In knowledge based systems, decisions and actions are based on the
manipulation of knowledge. (facts are compared and altered in some
manner). Manipulation is computational equivalent of reasoning.
Knowledge can be represented using various schemes. Eg. FOPL (First Order
Predicate Logic), and associative network.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 9
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 10/65
The knowledge base contains predefined concepts, domain constraints,
.
Knowledge representation approaches that can be used in learning systems are
•production rules
•Frames
•semantic networks
•predicate calculus
•vectors and matrices
•Graphs
•formal grammar
and
•procedural encoding
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 10
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 11/65
FOPL‐First order predicate logic
‐is a representation scheme for reasoning.
‐it comprises of symbols to represent statements.
Predicate – This s mbol returns a value of true or false. Ca ital
letters and words are used to represent predicates.
(eg.
A,
B,
NOT,
EQUAL) –
(eg. Has‐a , part‐of ,Father‐of)
Variables
ConstantsLogical quantifiers (Existential‐ there exist an x, Universal‐ for all x)
Connectors v‐dis unction ~ ‐ ne ation ^ ‐ con unction
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 11
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 12/65
Structure of machine learning system
The
machine
learning
system•the environment
•t e earning e ement
•the knowledge base
earn ng emen now e ge ase er ormance
Element
nv ronmen
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 12
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 13/65
Structure of machine learning system
Learning
Element Knowledge
Base Performance
ElementEnvironment
The environment supplies some piece of information to the learning element
explicit knowledge base
the erformance element uses the knowled e base to erform its task.
Finally, the information gained during performing the task can serve as a
feedback to the learning element.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 13
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 14/65
earn ng sys em may acqu re ru es o e av our, escr p ons o
physical objects, problem‐solving heuristics, classification
taxonomies over a sample space, and many other types of
knowledge useful in the performance of a wide variety of tasks.
Complex tasks require more knowledge than simple ones.
The knowledge base grows more in size, the problems of
complicated.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 14
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 15/65
Definition for Learning
A computer program is said to learn from experienceE
withrespect to some class of tasks T and performance measure P, if
, ,
experience E.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 15
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 16/65
A handwriting recognition learning problem
Task T: recognizing and classifying handwritten words within images
Performance measure P: Percent of words correctly classified
ra n ng exper ence : ata ase o an wr tten wor s w t g ven
classifications
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 16
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 17/65
A robot driving learning problem
Task T: driving on public four-lane highways using vision sensors.
Performance measure P: average distance travelled before an error
(as judged by human overseer)
Training experience E: a sequence of images and steering
commands recorded while observing a human driver.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 17
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 18/65
Some major issues
ome ma or ssues w c concern e process o earn ng now e ge n
general are:
data do not contain enough information a system cannot "learn" much
from it.
What should a system learn? In order to solve a particular problem, a
system has to learn specific features, dependencies relevant to the
solution, but not learn everything(Some irrelevant features only)
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 18
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 19/65
Some major issues
How to test how well the system has learned appropriate knowledge?
Testing the learning process is usually done through measuring thelearning error. The main approaches are:
Partitioning of data. A part of the data, say 70%, is used for training and
the other part for testing.
The leaving‐one‐out method means that we train n times the system
with (n ‐ 1) examples and check the system's reaction to the left‐out
exam le. After doin this n times we can calculate the correct answer of
the system as the ratio between the number of correctly processedexamples and the number n of all the examples
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 19
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 20/65
Machine learning
application areas of artificial intelligence (AI).
Machine learning is the study of making machines acquire new
knowledge, and recognize existing knowledge.
Machine learning is the capability of a computer to learn from
examples.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 20
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 21/65
Machine learning
performance criterion using example data or past experience.
With a model using some parameters, learning is the execution
of a computer program to optimize the parameters of the model.
future, or descriptive to gain knowledge from data, or both.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 21
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 22/65
Machine learning ‐ Goal
The goal of machine learning is to design programs that learnand/or discover, i.e. automatically improve their performance on
Successful learner
.
Makes general conclusions about the data it is trained on.
Act appropriately in new situations.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 22
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 23/65
Machine learning
There are certain patterns in the data.
,
construct a good and useful approximation.
at approx mat on may not exp a n everyt ng, ut may st
be able to account for some part of the data.
We believe that though identifying the complete process maynot be possible, we can still detect certain patterns or
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 23
.
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 24/65
Machine learning draws on concepts and results from
Statistics
Artificial intelli ence
Information theory
BiologyCognitive science
Computational complexity and
on ro
eory
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 24
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 25/65
Major types of Machine Learning
•Supervised learning
• nsuperv se earn ng
•Reinforcement learning
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 25
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 26/65
In supervised learning, there is a “teacher” that provides
the learner with a set of input‐output pairs.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 26
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 27/65
Process of supervised learning
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 27
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 28/65
Process of supervised learning
Training data
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 28
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 29/65
Decision Tress Classifier
Training data
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 29
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 30/65
In unsupervised learning, there is no teacher, providingdesired answers, but since the data are not entirely random,
can be applied in new cases.
Eg. Clustering
Algorithms
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 30
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 31/65
Reinforcement learning
Reinforcement learning corresponds to something between supervised andunsupervised approaches.
It differs from supervised learning in the sense that explicit input‐output pairs
are not available.
An agent explores environment and is able to take actions. Depending on the
outcome of the series of actions taken, the agent is rewarded or penalized.
Reinforcement learning is called “learning with critic”, as opposed to learningwith a teacher which is the supervised learning.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 31
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 32/65
Reinforcement learning
Reinforcement learning is called “learning with critic”, as opposed to learningwith a teacher which is the supervised learning.
Eg. Game playing is an important research area in both artificial intelligence
and machine
learning.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 32
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 33/65
Learning approaches are also classified as
Statistical (probabilistic or stochastic) methods
Connectionist methods/neural networks
Symbolic machine learning algorithms
ene c me o s an
Other hybrid approaches
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 33
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 34/65
Statistical methods
.
Perform some analysis which uses primarily the text characteristics without
.
Statistical techniques are
N‐
ram techni ues
Unsupervised clustering and
Hidden Markov model
that have been used for corpus‐based language analysis, probabilistic
grammar learning and lexicon‐building.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 34
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 35/65
Statistical methods
Naïve Bayes classifier
K-Nearest neighbour
Hidden Markov Model
xpec a on ax m sa on a gor m orwar - ac war
Maximum Entropy models
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 35
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 36/65
A neural network
A neural network is based on concepts of how the human brain is organised
and how it learns.
The nodes correspond to the neurons in the brain, and the links correspond
to the connections between neurons.
n or er o ma e pre c on, e neura ne wor accep s e va ues or e
predictors on the input nodes. These values are then multiplied by values
that are stored in the links, called weights.
These values are then added together at the output node, and a specialthreshold function is applied to get the prediction.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 36
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 37/65
Symbolic learning methods
Do not use probabilities explicitly
ec s on trees
Transformation based learning (TBL)
Inductive logic programming (ILP) and
.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 37
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 38/65
Minimum description length
MDL approaches aim to minimize the description of the set of words in the
input corpus.
,
defined as reducing the total length of a set of data.
Introducin a theor which can enerate certain data and thus serves as anabbreviation of the data set.
The implementation uses a learning mechanism which decreases the total
description length in each step.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 38
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 39/65
Genetic Algorithms (GAs)
Randomized search and optimization techniques
Guided by the principles of evolution and natural genetics
Efficient, adaptive and robust search processes
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 39
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 40/65
Hybrid Methods ‐ Ensemble Learning
examples.
,
the so called base classifier, by changing the training set or the input features
or the parameters of the classifier.
The predictions of all base classifiers are combined into a single final
prediction.
The idea builds on the assumption that combining the output of multipleexperts is better than the output of any single expert
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 40
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 41/65
Hybrid Methods ‐ Ensemble Learning
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 41
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 42/65
Selection of features
ac ne earn ng requ res se ec on o
Samples and features
Choice of algorithm.
Features are usuall re- rocessed.Dimensional reduction will be used to identify a subset of features, or
mathematical combinations of features that greatly reduces the size of the
machine learning problem.
A distance metric represents how far samples are separated from one another
in `feature space'. Proper representation schemes should be used for better
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 42
learning.
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 43/65
About Features
The input vector is called by a variety of names, some of these are input
vector, pattern vector, feature vector.
e componen s o e npu vec or are var ous y ca e ea ures,
attributes, input variables, and components.
The values of the components can be of three main types: real valued
numbers, discrete valued numbers, or categorical values .
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 43
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 44/65
About Features
The number of features in the instances determine the search space that needs
to be explored by the learning algorithm for a given classification task.
e presence o a arge num er o rre evan ea ures unnecessar y ncreases e
size of the search space, thus increasing the time needed for classification.
, ,
it difficult to extract knowledge such as classification rules in a way that is
comprehensible to humans. Conversely, the rules based on a small number of
relevant features are often concise easier to understand and use.
The most important reason behind feature selection is that it can eliminate the
effects of the curse of dimensionality.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 44
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 45/65
Applications of
machine
learning
Problems that can be solved by Machine learning are classified as
Association rule learnin
Classification
Prediction
Regression
Pattern recognition
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar
1/29/2012 45
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 46/65
Applications of
machine
learning
Association rule learning
One application of machine learning is basket analysis , which isfinding associations between products bought by customers:
If people who buy X typically also buy Y, and if there is a customer who
buys X and does not buy Y, he or she is a potential Y customer.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar1/29/2012 46
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 47/65
Applications of
machine
learning
Association rule learning
Finding an association rule‐
is learning a conditional probability of theform P(YIX)
where Y is the product we would like to condition on X, which is the
product or the set of products which we know that the customer has
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar1/29/2012 47
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 48/65
Applications of
machine
learning
Association rule learning
We may want to make a distinction among customers and toward this,estimate P(YIX, D)
where D is the set of customer attributes, for example, gender, age, marital
status, and so on.
If this is a bookseller instead of a supermarket, products can be books or
authors.
In the case of a Web portal, items correspond to links to Web pages, and wecan estimate the links a user is likely to click and use this information to
download such pages in advance for faster access.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar1/29/2012 48
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 49/65
Applications of
machine
learning
Classification
In
credit scoring,
the
bank
calculates
the
risk
given
the
amount
of
credit
and
the information about the customer.
This is an example of a classification problem where there are two classes:
low‐risk and high‐risk customers.
The information about a customer makes up the input to the classifier whose task is to assign the input to one of the two classes.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar1/29/2012 49
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 50/65
Applications of
machine
learning
Classification & Prediction
After training with the past data, a classification rule learned may be of the form
IF income>
p1
AND
savings>
p2
THEN
low
‐risk
ELSE
high
‐risk
It is a function that separates the examples of different classes.
predictions for novel instances , , if the future is similar to the past.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar1/29/2012 50
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 51/65
Applications of
machine
learning
Regression
In a system that can predict the price of a used car.
‐ , , , ,
information‐that we believe affect a car's worth.
.
Such problems where the output is a number are REGRESSION problems.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar1/29/2012 51
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 52/65
Applications of
machine
learning
Clustering
Clustering is grouping of input.
with a data of customers, a clustering model allocates customers similar in
their attributes to the same group.
Input : demographic information as well as the past transactions with the
.
the company may decide strategies, services and products, specific to
.
Such a grouping also allows identifying those who are outliers, namely, those
who are different from other customers.
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar1/29/2012 52
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 53/65
Applications of
machine
learning
in Pattern Recognition
2
1
Department of Electrical & Electronics EngineeringMuthiah Polytechnic College, Annamalainagar1/29/2012 53
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 54/65
Applications of
machine
learning
in Pattern Recognition
is recognizing characters from their images
Character recognition
• Printed character recognition (OCR) – Collection of dots
• Handwritten character recognition – Collection of dots and strokes
There are multiple classes - as many as the number of characters
A character image is not just a collection of random dots; it is a collection of strokes and has a regularity that we can capture by a learning program.
Department of Electrical & Electronics Engineering
Muthiah Polytechnic College, Annamalainagar1/29/2012 54
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 55/65
Applications of
machine
learning
Sequence learning
A word is a sequence of characters and successive characters are notindependent but are constrained by the words of the language.
This has the advantage that even if we cannot recognize a character, we can
still read t?e word.
Such contextual dependencies may also occur in higher levels, between
words and sentences, through the syntax and semantics of the language.
There are machine learning algorithms to learn sequences and model suchdependencies
Department of Electrical & Electronics Engineering
Muthiah Polytechnic College, Annamalainagar1/29/2012 55
equence earn ng me o s are a so use n o n orma cs
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 56/65
Applications of
machine
learning
Face Recognition
In Face recognition
•the classes are people to be recognized
•the learning program should learn to associate the face images to
identities.
Department of Electrical & Electronics Engineering
Muthiah Polytechnic College, Annamalainagar1/29/2012 56
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 57/65
Applications of
machine
learning
The input is acoustic
The classes are words that can be uttered
This time the association to be learned is from an acoustic signal to a word
.
Department of Electrical & Electronics Engineering
Muthiah Polytechnic College, Annamalainagar1/29/2012 57
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 58/65
Applications of
machine
learning
Medical diagnosis The inputs are the relevant information about the patient
The inputs contain the patient's age, gender, past medical history, and
.
Department of Electrical & Electronics Engineering
Muthiah Polytechnic College, Annamalainagar1/29/2012 58
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 59/65
Applications of
machine
learning
Outlier detection
Another use of machine learning is outlier detection, .
After learning the rule, we are not interested in the rule but the exceptions
,
.
Department of Electrical & Electronics Engineering
Muthiah Polytechnic College, Annamalainagar1/29/2012 59
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 60/65
Applications of
machine
learning
Outlier detection
Learning a rule from data allows knowledge extraction. ,
we have an explanation about the process underlying the data.
‐ ‐,
risk customers (Credit scoring) we have the knowledge of the properties of
low‐risk customers.
We
can
then
use
this
information
to
target
potential
low‐
risk
customers
more efficiently.
Department of Electrical & Electronics Engineering
Muthiah Polytechnic College, Annamalainagar1/29/2012 60
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 61/65
Applications of
machine
learning
Compression
Learning also performs compression by fitting a rule to the data.
,
to store and less computation to process.
.
,
sum of every possible pair of numbers.
Department of Electrical & Electronics Engineering
Muthiah Polytechnic College, Annamalainagar1/29/2012 61
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 62/65
Applications of
machine
learning
Networking & Communications
In networking and telecommunications, call patterns and traffic data are
analyzed for network optimization and maximizing the quality of service.
Department of Electrical & Electronics Engineering
Muthiah Polytechnic College, Annamalainagar1/29/2012 62
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 63/65
Applications of machine learning in
Natural Lan ua e Processin
Machine learning also helps us find solutions for many natural language
processing tasks.
Text categorisation
Text summarisation
Word segmentation
tagg ng
Parsing
Word sense disambiguation
Unknown word recognitionSpeech recognition and
Department of Electrical & Electronics Engineering
Muthiah Polytechnic College, Annamalainagar1/29/2012 63
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 64/65
Applications of machine learning in
Natural Lan ua e Processin
Machine learning also helps us find solutions for many natural language
processing tasks.
anguage en ca on
From text
From Speech
Speaker Diarization
To identify the speaker changes and the speaker clusters, and to estimate the
number of speakers involved in the document
To be
able
to
process
speech
documents
as
well
as
documents
containing
music, silence, and other sounds.
Department of Electrical & Electronics Engineering
Muthiah Polytechnic College, Annamalainagar1/29/2012 64
8/2/2019 Prof. K. Rajan
http://slidepdf.com/reader/full/prof-k-rajan 65/65
Speaker Diarization
•To identify the speaker turns and the speaker clusters, and to estimate the
number of
speakers
involved
in
the
document,
without
any
priori
information.
• To e a e to process speec ocuments as we as ocuments containing music,
silence, and other sounds.
Department of Electrical & Electronics Engineering
Muthiah Polytechnic College, Annamalainagar1/29/2012 65
top related