Intro - Types of Machine Learning

An
Introduction
to
Machine Learning Techniques
(KCS-055)
By
Manish Kumar Sharma
Assistant Professor (Senior Scale), IT Department
ABES Engineering College, Ghaziabad
At the end of course , the student will be able:
-To understand the need for machine learning for
various problem solving .
-To understand a wide variety of learning algorithms
and how to evaluate models generated from data.
-To understand the latest trends in machine learning.
-To design appropriate machine learning algorithms and
apply the algorithms to a real-world problems.
-To optimize the models learned and report on the
expected accuracy that can be achieved by applying the
models.
 Probability
 distribution, random variable, expectation, conditional
probability, variance, density
 Linear algebra
 matrix multiplication
 eigenvector
 Basic programming (in Python)
This is a mathematically intense course.

But that’s why it’s exciting and rewarding!
Machine Learning
Study of algorithms that
improve their performance
at some task
with experience
5
Arthur Samuel (1959): Machine Learning is the
field of study that gives the computer the ability
to learn without being explicitly programmed.
Photos from Wikipedia

Tom Mitchell (1998): 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.
Task (T): Playing Chess

Experience (E)(data): games played by the
program (with itself)
Performance measure (P): winning rate
Image from Tom Mitchell’s homepage

 Handwriting recognition learning problem
• Task T: Recognizing and classifying handwritten words
within images
• Performance P: Percent of words correctly classified
• Training experience E: A dataset of handwritten
words with given classifications
A chess learning problem
• Task T: Playing chess
• Performance measure P: Percent of games won
against opponents
• Training experience E: Playing practice games against
itself.
A robot driving learning problem
• Task T: Driving on highways using vision sensors
• Performance measure P: Average distance traveled
before an error
• training experience: A sequence of images and
steering commands recorded while observing a human
driver.
Note: A computer program which learns from experience is called a machine
learning program or simply a learning program. Such a program is sometimes
also referred to as a learner.
Basic components of learning process
whether by a human or a machine, can be divided into
four components, namely
-Data storage,
-Abstraction,
-Generalization and
-Evaluation
Application of machine learning methods to large databases is
called data mining. In data mining, a large volume of data is
processed to construct a simple model with valuable use, for
example, having high predictive accuracy.
 The following is a list of some of the typical applications of machine learning.
 1. In retail business, machine learning is used to study consumer behavior.
 2. In finance, banks analyze their past data to build models to use in credit
applications, fraud detection, and the stock market.
 3. It is used to find solutions to many problems in vision, speech recognition,
and robotics.
 4. In medicine, learning programs are used for medical diagnosis.
 5. In telecommunications, call patterns are analyzed for network optimization
and maximizing the quality of service.
14
 6. In science, large amounts of data in physics, astronomy, and biology can only
be analyzed fast enough by computers. The World Wide Web is huge; it is
constantly growing and searching for relevant information cannot be done
manually.
 7. In artificial intelligence, it is used to teach a system to learn and adapt to
changes so that the system designer need not foresee and provide solutions for
all possible situations.
 8. Machine learning methods are applied in the design of computer-controlled
vehicles to steer correctly when driving on a variety of roads.
 9. Machine learning methods are applied in Character recognition: Different
handwriting styles.
 10. Machine learning methods have been used to develop programmes for
playing games such as chess, backgammon and Go etc.
Training examples of a person
Test images
16
Machine learning algorithms can be classified into three
types.
Supervised learning
Unsupervised learning
Reinforcement learning
Supervised learning is the machine learning task of learning a
function that maps an input to an output based on example input-
output pairs.
In supervised learning, each example in the training set is a pair
consisting of an input object (typically a vector) and an output
value.
A supervised learning algorithm analyzes the training data and
produces a function, which can be used for mapping new
examples.
In the optimal case, the function will correctly determine the class
labels for unseen instances. Eg. Classification and Regression problems
A wide range of supervised learning algorithms are available, each
with its strengths and weaknesses. There is no single learning
algorithm that works best on all supervised learning problems.
Remarks : A “supervised learning” is so called because the process of an
algorithm learning from the training dataset can be thought of as a
teacher supervising the learning process. We know the correct answers
(that is, the correct outputs), the algorithm iteratively makes predictions
on the training data and is corrected by the teacher. Learning stops when
the algorithm achieves an acceptable level of performance.
Unsupervised learning is a type of machine learning algorithm
used to draw inferences from datasets consisting of input data
without labeled responses.
In unsupervised learning algorithms, a classification or
categorization is not included in the observations.
There are no output values and so there is no estimation of
functions. Since the examples given to the learner are unlabeled,
the accuracy of the structure that is output by the algorithm
cannot be evaluated.
The most common unsupervised learning method is cluster
analysis, which is used for exploratory data analysis to find hidden
patterns or grouping in data.
Reinforcement learning is the problem of getting an agent to act
in the world so as to maximize its rewards.
A learner (the program) is not told what actions to take as in most
forms of machine learning, but instead must discover which
actions yield the most reward by trying them.
In the most interesting and challenging cases, actions may affect
not only the immediate reward but also the next situations and,
through that, all subsequent rewards.
 For example, consider teaching a dog a new trick: we cannot tell
it what to do, but we can reward/punish it if it does the
right/wrong thing. It has to find out what it did that made it get
the reward/punishment.
We can use a similar method to train computers to do many tasks,
such as playing backgammon or chess, scheduling jobs, and
controlling robot limbs.
Reinforcement learning is different from supervised learning.
Supervised learning is learning from examples provided by a
knowledgeable expert.
Thank you!

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An

This is a mathematically intense course.

Photos from Wikipedia

Task (T): Playing Chess

Image from Tom Mitchell’s homepage

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