introduction to machine learning

1. Department of Computer Science &
Engineering
(July – Dec 2020)
8/28/20201

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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

3.  “Learning denotes changes in a system that ...
enable a system to do the same task … more
efficiently the next time.”
 “Learning is constructing or modifying
representations of what is being experienced.”
 “Learning is making useful changes in our minds.”
“Machine learning refers to a system capable of
the autonomous acquisition and integration of
knowledge.”
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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

6.  “Field of study that gives computers the ability
to learn without being explicitly programmed”
• Arthur Samuel (1959)
 “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”
• Tom M. Mitchell (1998)
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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

8. Machine learning is a subfield of computer
science that explores the study and
construction of algorithms that can learn
from and make predictions on data.
Such algorithms operate by building a model
from example inputs in order to make data-
driven predictions or decisions, rather than
following strictly static program instructions
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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

10.  “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”
 In our project,
• T: classify emails as spam or not spam
• E: watch the user label emails as spam or not spam
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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

11. Facial recognition
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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

12. Self-customizing programs (Netflix,
Amazon, etc.)
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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

13.  No human experts
• industrial/manufacturing control
• mass spectrometer analysis, drug design, astronomic discovery
 Black-box human expertise
• face/handwriting/speech recognition
• driving a car, flying a plane
 Rapidly changing phenomena
• credit scoring, financial modeling
• diagnosis, fraud detection
 Need for customization/personalization
• personalized news reader
• movie/book recommendation
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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

15. Supervised learning : Learn by examples as
to what a face is in terms of structure, color,
etc so that after several iterations it learns
to define a face.
Unsupervised learning : since there is no
desired output in this case that is provided
therefore categorization is done so that the
algorithm differentiates correctly between
the face of a horse, cat or human.
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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

16. REINFORCEMENT LEARNING:
Learn how to behave successfully to
achieve a goal while interacting with an
external environment .(Learn via
Experiences!)
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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

17.  Supervised learning is the machine learning
task of inferring a function from labeled
training data. The training data consist of a
set of training examples. In supervised
learning, each example is a pair consisting of
an input object and a desired output value. A
supervised learning algorithm analyzes the
training data and produces an inferred
function, which can be used for mapping new
examples.
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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

18. Supervised Learning
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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

20.  Regression means to predict the output value
using training data.
 Classification means to group the output into
a class.
 e.g. we use regression to predict the house
price from training data and use
classification to predict the Gender.
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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

21. AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM
FARIDABAD 21

22. Applications for supervised
Learning
•Risk assessment - Supervised learning is used to assess the risk
in financial services or insurance domains in order to minimize the
risk portfolio of the companies.
•Image classification - Image classification is one of the key use
cases of demonstrating supervised machine learning. For example,
Facebook can recognize your friend in a picture from an album of
tagged photos.
•Fraud detection - To identify whether the transactions made by the
user are authentic or not.
•Visual recognition - The ability of a machine learning model to
identify objects, places, people, actions and images.
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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

23. Unsupervised Machine
Learning
In Unsupervised Learning, the machine uses
unlabeled data and learns on itself without any
supervision. The machine tries to find a pattern
in the unlabeled data and gives a response.
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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

24. Supervised and
Unsupervised
Learning
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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

25. 8/28/202025
Aravali College of Engineering And Management
Jasana, Tigoan Road, Neharpar, Faridabad, Delhi NCR
Toll Free Number : 91- 8527538785
Website : www.acem.edu.in