The document provides an overview of machine learning, detailing its history, types, and applications. It distinguishes between supervised, unsupervised, semi-supervised, and reinforcement learning, explaining how each works and their relevance in various tasks. Additionally, it compares machine learning with deep learning, highlighting their operational differences and the conditions under which each performs optimally.

1. Machine Learning Basics
Chapter 1
Presented to
Prof. Vibhakar Mansotra
Dean of Mathematical science
Presented by
Akanksha Bali
Research Scholar, Dept of Computer science and IT

2. Contents
• Introduction
• Types of machine learning
• Supervised Learning
• Unsupervised Learning
• Semi Supervised Learning
• Reinforcement Learning
• Applications
• Machine learning vs Deep Learning

3. Introduction (When, What and Why)
• The term Machine Learning was coined by Arthur Samuel in 1959, an
american pioneer in the field of computer gaming and artificial intelligence
and stated that “ it gives computers the ability to learn without being
explicitly programmed” And in 1997, Tom Mitchell gave a “ well-Posed”
mathematical and relational definition that “ A Computer Program is said
to learn from experience E with respect to some task T and some
performance measure P, if its performance on T, as measured by P,
improves with experience E”.
• Why do we need Machine Learning?
Machine learning is needed for tasks that are too complex for humans to
code directly. So instead, we provide a large amount of data to a machine
learning algorithm and let the algorithm work it out by exploring that data
and searching for a model that will achieve what the programmers have set
it out to achieve.

4. Example
Experience Salary
2 3,00,000
4 6,00,000
6 9,00,000
10 15,00,000
12 24,00,000
14 28,00,000
If (experience < = 10)
{
Salary = experience *1.5*100000
}
Else if ( experience >10)
{ salary = experience * 2*100000

5. Types of Machine Learning
• Supervised Learning
• Unsupervised Learning
• Semi Supervised Learning
• Reinforcement Learning

6. Supervised Learning
• This algorithm contains a target variable which is to be predicted from any
given set of predictors. Using these set of variables, we can generate a
function that maps inputs to the desired outputs. This Process Continues
until a model achieves a desired level of accuracy on a training data
• Supervised Learning can be further grouped into classification and
regression
• Examples: KNN, Logistic Regression, Decision Tree, Random forest etc
• For the spam detection task, we consider a setting in which the learner
receives training emails for which the label spam/ non spam is provided.
On the basis of such training, the learner should figure out a rule for
labelling a newly arriving email message.

7. Environment
Actual Result Desired Result
Error
Learning Agent
Supervisor

8. Unsupervised Learning
• In unsupervised learning, we dont have any outcome variable to predict
• In unsupervised learning, there is no correct answers and there is no
teacher.
• It can be further grouped into clustering and association problem
• Examples: K-Means, Apriori Algorithm
• Clustering a dataset into subsets of similar objects is a typical example of
such a task.

9. Semi-Supervised Learning
• It falls between supervised and unsupervised learning.
• In semi-supervised learning, an algorithm learns from a dataset that
includes both labelled and unlabelled data, usually mostly unlabelled.
• Why semi-supervised learning is important?
when you don't have enough labelled data to produce an accurate model
and you don't have the ability or resources to get more, you can use semi
supervised technique.
• Example: photo archive where only some of the images are labelled (eg.
Dog, cat) and the majority are unlabelled.

10. Reinforcement Learning
• In this algorithm, the machine is trained to make a specific decisions. It
works in such a way as a machine is exposed to an environment such that it
trains y itself and continually using trial error.
• This machines learns from a past experience and tries to capture all the best
possible knowledge for accurate decision.
• Example: game playing, robot navigating
Starting
Location
Goal

11. Applications
• Virtual Personal Assistants
a) Smart speakers: Amazon Echo and google
b) Smartphones: samsung bixby on samsung s8
c) Mobile Apps:Google Allo
• Predictions while commuting
a) Traffic Predictions
b) Online transportation networks
• Video Surveillance
• Social Media Services
a) people you may know
b) Face recognition
• Email Spam and Malware Filtering
• Online Customer Support
• Search engine result refining
• Product Recommendations
• Online Fraud detection

12. Deep Learning vs. Machine Learning
Machine Learning Deep Learning
It uses algorithms to parse data, learn from that
data and make informed decisions based on
what it has been learned
Deep learning structures algorithms in layers to
create an artificial neural network that can learn
and make intelligent decisions on its own.
When the data is small, machine learning
algorithm perform well
When the data is small, deep learning algorithm
dont perform that well.
It depends on low end machine It depends on high end machine
Machine learning do a small amount of matrix
multiplication operation
Deep learning do a large amount of matrix
multiplication operation
In machine learning, most of the applied
features need to be identified by an expert and
then handcoded as per the domain and datatype
Deep learning algorithms try to learn high level
features from data.
Machine learning comparatively takes much
less time to train, ranging from a few seconds to
a few hours. But testing time increases on
increases the size of data.
It takes a long time to train but less time to test.

13. References
• https://www.geeksforgeeks.org/machine-learning/
• Ethem Alpaydn, “ introduction to machine
learning”, MIT Press Cambridge, Massachusetts
london, England, second edition, 2010.