The document discusses different types of machine learning including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of each type, such as using labeled data to classify emails as spam or not spam for supervised learning, grouping fruits by color without labels for unsupervised learning, and using rewards to guide an agent through a maze for reinforcement learning. The document also covers applications of machine learning across different domains like banking, biomedical, computer, and environment.

1. GENBA SOPANRAO MOZE COLLEGE
OF ENGINEERING
DEPARTMENT OF COMPUTER
ENGINEERING
Savitribai Phule Pune University
(SPPU)(2015 Course)
Academic Year 2020-2021
Fourth Year of Computer Engineering
Subject Code 410250
Subject- Machine Learning
- by Prof.Amruta Aphale

2. Unit.1 Introduction to
Machine Learning

3. Machine Learning
• Herbert Alexander Simon:
“Learningisanyprocessby which a
system improves performance
from experience.”
• “Machine Learning is concerned
with computer programs that
automatically improve their
performance through experience.
“
Herbert Simon
Turing Award 1975
Nobel Prize in Economics
1978

4. 4
Why Machine Learning?
• Develop systems that can automatically adapt and
customize themselves to individual users.
– Personalized news or mail filter
• Discover new knowledge from large databases.
– Market basket analysis
• Ability to mimic human and replace certain
monotonous tasks -
which require some intelligence.
• like recognizing handwritten characters

5. When Machine Learning?
• Expensive System to construct manually
because they require specific detailed skills
tuned to a specific task
• Human expertise does not exist (navigating on
Mars),
• Humans are unable to explain their expertise
(speech recognition)
• Solution changes in time (routing on a
computer network)
• Solution needs to be adapted to particular
cases (user biometrics)

6. 6
Why ML now?
• Flood of available data (especially with the
advent of the Internet)
• Increasing computational power
• Growing progress in available algorithms and
theory developed by researchers
• Increasing support from industries

7. 7
Machine Learning: definition
 Machine Learning is concerned with the
development, the analysis, and the application of
algorithms that allow computers to learn
Learning:

A computer learns if it improves its
performance at some task with experience
(i.e. by collecting data)
Extracting a model of a system from the sole
observation (or the simulation) of this system in
some situations.
A model = some relationships between the
variables used to describe the system.

8. Goals of Machine Learning
Two main goals:
1. make prediction
2. Better understand the system

9. Lets see how it works like brain
• How did our brain process the images?
• How did the grouping happen?

10. Observe

11. What we observed ?
• Human brain processed the given images -
learning
• After learning the brain simply looked at
the new image and compared with the
groups classified the image to the closest
group - Classification
• If a machine has to perform the same
operation we use Machine Learning

12. The main advantage of Machine
Learning
• Learning and writing an algorithm
• Its easy for human brain but it is tough for a
machine. It takes some time and good
amount of training data for machine to
accurately classify objects
• Implementation and automation
• This is easy for a machine. Once learnt a
machine can process one million images
without any fatigue where as human brain
can’t .That’s why ML with bigdata is a deadly
combination
Bigdata
Analytics
Venkat

13. What We Talk About When We Talk
About “Learning”
Learning general models from a data of particular
examples
Data is cheap and abundant (data warehouses, data
marts)
Example in retail: Customer transactions to consumer
behavior:
Build a model that is a good and useful approximation
to the data.

14. How ML will work on fruit slide ?
we have a dataset that contains pictures of different
kinds of fruits and we want Machine Learning to
segregate the photos based on the kind of fruits.
First we provide the dataset to the system i.e we
provide the input data.
The system goes through the entire dataset or
analyses it to find patterns based on size, shapes,
colors, etc.

15. How ML will work on fruit slide ?
Now that it has figured out the patterns, the systems
takes decisions and starts separating the photos
based on the patterns.
Once the work is done, the system learns from the
feedback it gets.
If it gets any of the fruit type wrong, it will make sure
it does not happen in the future.

16. The Learning
Process

17. ML
Applications

19. Applications of MachineLearning
• Banking / Telecom / Retail
• Identify:
• Prospective customers
• Dissatisfied customers
• Good customers
• Bad payers
• Obtain:
• More effective advertising
• Less credit risk
• Fewer fraud
• Decreased churn rate
Bigdata
Analytics
Venkat
19

20. Applications of MachineLearning
• Biomedical / Biometrics
• Medicine:
• Screening
• Diagnosis and prognosis
• Drug discovery
• Security:
• Face recognition
• Signature / fingerprint / iris verification
• DNA fingerprinting
Bigdata
Analytics
Venkat
20

21. Applications of MachineLearning
• Computer / Internet
• Computer interfaces:
• Troubleshooting wizards
• Handwriting and speech
• Brain waves
• Internet
• Hit ranking
• Spam filtering
• Text categorization
• Text translation
• Recommendation
Bigdata
Analytics
Venkat
21

23. Environment of Classical System

24. Adaptive System

25. Adaptive Learning
Spam filtering, Natural Language Processing, visual
tracking with a webcam or a smartphone, and predictive
analysis are only a few applications that revolutionized
human-machine interaction and increased our
expectations.
Such a system isn't based on static or permanent
structures (model parameters and architectures) but
rather on a continuous ability to adapt its behavior to
external signals (datasets or real-time inputs) and, like a
human being, to predict the future using uncertain and
fragmentary pieces of information.

26. The concept of learning in a ML system
• Learning = Improving with experience at some task
– Improve over task T,
– With respect to performance measure, P
– Based on experience, E.
7

27. Spam Filtering
Example: Spam Filtering
Spam - is all email the user does not
want to receive and has not asked to
receive
T: Identify Spam Emails
P:
% of spam emails that were filtered
% of ham/ (non-spam) emails that
were incorrectly filtered-out
E: a database of emails that were
labelled by users

28. Email Spam Classification
• The Input: Database of emails, some with human-
given labels
• Objective Function: Percentage of email messages
correctly classified.
• The Output: Categorize email messages as spam or
legitimate.

30. Supervised Learning: Uses
Prediction of future cases: Use the rule to predict the
output for future inputs
Knowledge extraction: The rule is easy to understand
Compression: The rule is simpler than the data it
explains
Outlier detection: Exceptions that are not covered by
the rule
Example Application:
Predict loan approval
Weather prediction

31. • fruits are apple,banana,cherry,grape.
• so you already know from your previous work
that, the shape of each and every fruit so it is
easy to arrange the same type of fruits at one
place.
• here your previous work is called as train data in
data mining.
Supervised learning on fruit slide

32. Supervised learning on fruit slide
• so you already learn the things from your
train data, This is because of you have a
response variable which says you that if
some fruit have so and so features it is
grape, like that for each and every fruit.
• This type of data you will get from the train
data.
• This type of learning is called as Supervised
learning.

33. Unsupervised Learning:Uses
Learning “what normally happens”
No output
Clustering: Grouping similar instances
Example applications
Customer segmentation in CRM
Image compression: Color quantization
Bioinformatics: Learning motifs

34. Unsupervised learning on fruit slide
your task is to arrange the same type fruits at one
place.
This time you don't know any thing about that
fruits, you are first time seeing these fruits so
how will you arrange the same type of fruits.
• What you will do first you take on fruit and you
will select any physical character of that
particular fruit. suppose you taken colours.

35. Unsupervised learning on fruit slide
• Then the groups will be some thing like this.
• RED COLOR GROUP: apples &
cherry fruits.
• GREEN COLOR AND SMALL SIZE:
grapes.
• This type of learning is know unsupervised
learning.

36. Reinforcement Learning
36
Learning a policy: A sequence of outputs
No supervised output but delayed
Reward
Examples Aplication:
Credit assignment problem
Game playing
Robot in a maze
Multiple agents, partial observability, ...

37. Reinforcement Learning:Uses
Learning a policy: A sequence of outputs
No supervised output but delayed reward
Credit assignment problem
Example Application
Game playing
Robot in a maze
Multiple agents