aasthabudhiraja

Introduction To Machine Learning

Aravali College of Engineering and Management, Faridabad

Department of Computer Science & Engineering(July – Dec 2020)

8/27/2020

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Overview

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

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 Element

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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Learning system in Machine Learning

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Existence of 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

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

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Speech Recognition

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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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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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8/27/2020

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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

aasthabudhiraja

Education

engineering

management