Acem machine learning

Department of Computer Science &
Engineering
(July – Dec 2020)
8/27/20201

What is Learning?
What is machine learning?
Applications of machine learning
Types of algorithms
Basics of statistical pattern recognition
• Preprocessing
• Feature Extraction/Selection
• Learning
Supervised and Unsupervised Learning
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AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

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 “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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Design affected by:
performance element used
• e.g., utility-based agent, reactive agent, logical
agent
functional component to be learned
• e.g., classifier, evaluation function, perception-
action function,
representation of functional component
• e.g., weighted linear function, logical theory, HMM
feedback available
• e.g., correct action, reward, relative preferences
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 “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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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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 “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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Facial recognition
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Self-customizing programs (Netflix,
Amazon, etc.)
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Speech Recognition
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 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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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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REINFORCEMENT LEARNING:
Learn how to behave successfully to
achieve a goal while interacting with an
external environment .(Learn via
Experiences!)
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 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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Supervised Learning
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 Learning (training): Learn a model using
the training data
 Testing: Test the model using unseen test
data to assess the model accuracy
 Accuracy= No. of correct classifications
Total no of test cases
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In order to solve a given problem of supervised learning, one has
to perform the following steps:
1. Determine the type of training examples. Before doing anything
else, the user should decide what kind of data is to be used as a
training set.
2. Gather a training set. Thus, a set of input objects is gathered
and corresponding outputs are also gathered, either from
human experts or from measurements.
3. Determine the structure of the learned function and
corresponding learning algorithm.
4. Complete the design. Run the learning algorithm on the
gathered training set.
5. Evaluate the accuracy of the learned function. After parameter
adjustment and learning, the performance of the resulting
function should be measured on a test set that is separate from
the training set
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 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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Classification - Supervised
Learning
Classification is used when the output variable is categorical i.e. with 2
or more classes. For example, yes or no, male or female, true or false,
etc
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Regression - Supervised
Learning
Regression is used when the output variable is a real or continuous value. In
this case, there is a relationship between two or more variables i.e., a change
in one variable is associated with a change in the other variable. For
example, salary based on work experience or weight based on height, etc.
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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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discriminating human faces from non faces.
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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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Supervised and Unsupervised
Learning
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8/27/202033
Aravali College of Engineering And Management
Jasana, Tigoan Road, Neharpar, Faridabad, Delhi NCR
Toll Free Number : 91- 8527538785
Website : www.acem.edu.in

Acem machine learning

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