Aravali college of Engineering and Management, Faridabad (4)

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Aravali College of
Engineering and Management, Faridabad

• Department of Computer Science
  Engineering(July Dec 2020)

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Learning
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What is Learning?

• 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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Learning Process
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Learning system in Machine Learning
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Machine Learning

• 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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Understanding Machine Learning from daily life
applications
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Machine Learning

• 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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Understanding the Concept of Machine Learning
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Example Spam Mail Detection

• 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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Applications of Machine Learning

• Facial recognition

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Applications of Machine Learning

• Self-customizing programs (Netflix, Amazon, etc.)

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Why Machine Learning?

• 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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How Machine Learning Different from Artificial
Intelligence
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Types Of Machine Learning

• 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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Types of Machine Learning

• 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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• 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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• 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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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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Aravali College of Engineering And
Management Jasana, Tigoan Road, Neharpar,
Faridabad, Delhi NCR Toll Free Number 91-
8527538785 Website  www.acem.edu.in