Machine learning is a subfield of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computer systems to learn and make predictions or decisions without being explicitly programmed. In essence, machine learning allows computers to automatically discover patterns, associations, and insights within data and use that knowledge to improve their performance on a task.

1. Machine Learning
Introduction
By: Dr. Shweta Saraswat

2. Contents
• Objective
• Scope
• Outcome of the Course
• Introduction to AI and ML
• Relationship between ML and Data Science
• Applications of Machine Learning
• Prerequisites

4. Machine Learning
• Definition: “ Machine Learning is a sub domain of
Artificial Intelligence that enables software programs
to improve their prediction accuracy without even
being expressly designed to do so.”

5. Objective of this syllabus:
• This module aims to provide students with an in-
depth introduction of various algorithms of machine
learning and their practical implementation.

6. Objective of Machine Learning
• The primary purpose of machine learning is to
discover patterns in the user data and then make
predictions based on these and intricate patterns for
answering business questions and solving business
problems.
• Machine Learning helps in analyzing the data as well
as identifying trends.

7. Scope
• It provides the machine a capability to acquire
knowledge which makes it a more human like
machine that’s why it is using in so many domains as
one might imagine.

8. Outcome
• By the end of this syllabus students should be able to:
• Develop an appreciation for what is involved in learning
models from data
• Understand a wide variety of learning algorithms
• Understand how to evaluate models generated for data
• Apply the algorithm to real problem , optimize models
learned and report on the expected accuracy that can be
achieved by applying the models.

9. INTRODUCTION TO AI AND ML
• Topic 1: Emergence of Artificial Intelligence
Science associated with data is going toward a new paradigm where one can teach
machines to
learn from data and derive a variety of useful insights giving rise to Artificial Intelligence.

10. INTRODUCTION TO AI AND ML
• History:
• Artificial Intelligence first coined by John Mcharthy in
1956.
• Machine Learning first coined by Arthur Lee Samuel
in 1959.
• Deep Learning first coined by Geoffrey Hinton.

11. Relationship between AI, ML and
Deep Learning

28. Machine Learning at 21st century
• 2006: In the year 2006, computer scientist Geoffrey Hinton has given a new name to
neural net research as "deep learning," and nowadays, it has become one of the most
trending technologies.
• 2012: In 2012, Google created a deep neural network which learned to recognize the
image of humans and cats in YouTube videos.
• 2014: In 2014, the Chabot "Eugen Goostman" cleared the Turing Test. It was the first
Chabot who convinced the 33% of human judges that it was not a machine.
• 2014: DeepFace was a deep neural network created by Facebook, and they claimed
that it could recognize a person with the same precision as a human can do.
• 2016: AlphaGo beat the world's number second player Lee sedol at Go game. In 2017
it beat the number one player of this game Ke Jie.
• 2017: In 2017, the Alphabet's Jigsaw team built an intelligent system that was able to
learn the online trolling. It used to read millions of comments of different websites to
learn to stop online trolling.

29. Machine Learning at present:
• Now machine learning has got a great advancement in its
research, and it is present everywhere around us, such
as self-driving cars, Amazon
Alexa, Catboats, recommender system, and many more.
• It includes Supervised, unsupervised, and reinforcement
learning with clustering, classification, decision tree, SVM
algorithms, etc.
• Modern machine learning models can be used for making
various predictions, including weather prediction, disease
prediction, stock market analysis, etc.

30. Prerequisites
• Before learning machine learning, you must have the basic
knowledge of followings so that you can easily understand
the concepts of machine learning:
• Fundamental knowledge of probability and linear algebra.
• The ability to code in any computer language, especially in
Python language.
• Knowledge of Calculus, especially derivatives of single
variable and multivariate functions.

32. Book Reference
32
S. No. Title of Book Authors Publisher
Reference Books
T1 Pattern Recognition and Machine
Learning
Christopher M. Bishop Springer
T2 Introduction to Machine Learning
(Adaptive Computation and
Machine Learning),
Ethem Alpaydın MIT Press
Text Books
R1 Machine Learning Mitchell. T, McGraw Hill