This is the first lecture of the detailed course of machine learning.

1. Introduction to Machine Learning-1
Dr. Varun Kumar
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2. Outlines
1 Introduction to Learning
2 Motivation:
3 Scope of Machine Learning:
4 References
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3. Introduction to Learning:
Key Features of Learning:
1 Learning is a slow and steady process.
2 Learning is a feedback based approach.
3 It is a process for acquiring more information related to the given
problem.
4 Learning provides a methodology for solving any complex challenges.
5 Any problem can be solved quickly, when the probability of learning is
very high.
6 Fast learning is the current demand in global scenario for solving the
complex problem.
7 Lack of fast learning ability in current scenario provides a new
research direction, ie Machine Learning.
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4. Motivation:
Motivation:
1 Can computer take decisions (even smart smarter ones) just like
humans ?
2 Can computer help human in doing their tasks of daily living ?
3 Can we build a smart eco-system where users get feedback and
systems can update their actions ?
4 Can we develop technology to learn from human behavior ?
5 Repetitive task modeling
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5. Scope of this Subject
Scope of this Subject:
1 5G wireless communication
2 Artificial Intelligence
3 E-commerce
4 Industrial 4.0
5 Internet of Thing
6 Health Care Support System
7 Modern Agriculture
8 Satellite Communication
9 DNA Synthesis and Related Research
Many more
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6. Learning Intuition
Conventional Approach: There is a requirement of proper algorithm (a
set of instruction) for solving a problem. Ex- Sorting numbers
Non-conventional Approach: Some real time problem cannot be solved
mathematically or through algorithmic process. Ex- To check a spam
emails from legitimate emails.
There are many applications for which we do not have an algorithm
but do have example data.
Ex- A supermarket chain (For successful operation: Need a advance learning )
i. Hundreds of stores all over a country
ii. Selling thousands of goods
iii. Millions of customers
iv. Details of each transaction: date, customer identification code, goods
bought and their amount, total money spent
v. Consumption of gigabytes of data every day
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7. Continued–
Challenges of Supermarket Chain:
1 To increase the selling product
2 To find out the potential customer
3 To follow adaptive pattern of marketing, which changes in time and
by geographic location
Many more
Usage of Machine Learning:
1 Stored data (details of transaction): Need a rigorous analysis and
turned into information.
2 This information helps for short and long term prediction.
Note: Behavioral pattern of selling a product has not a fixed algorithm
and does not follow any mathematical expression.
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8. Example:
Tele-communication:
Call patterns are analyzed for network optimization and maximizing the
quality of service.
Key Wireless Resources:
1 Power
2 Time
3 Frequency
4 Space (Number of antenna element across Tx /Rx )
5 Code
Challenges of Tele-communication
Reduction of recurring cost (fuel cost + maintenance)
Improvisation of quality of service at low power and less bandwidth.
QoS → high data rate, minimum latency, better link reliability
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9. Example:
Wireless Resources Allocation Using ML: Why ??
Network Densfication
Availability of superior computational resource
Prediction of active users in a geographical bound
Many more
Finance:
Financial institution (Bank) has a strategically importance in any country.
Business Model of Financial Institution:
Give & Take
Output > Input → Profit & Output < Input → Loss
Aim of Financial Institution:
Maximizing the profit: Saving account (4%) & On loan (14-18%)
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10. Example:
Minimizing the loss:
Technology driven network
Reduction of operational expenditure
Earlier prediction of defaulter customer
Developing a mechanism for preventing non-performing assets (NPA)
Finding a potential robust customer using ML
Source of income.
Current debt on customer.
Banks analyze customer’s past data (repay history of previous loan).
Bank also analyzes the loan amount.
To build models to use in credit applications.
Predict the chance for willful defaulter.
Many more
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11. Example:
Manufacturing:
Industrial manufacturing plays a vital role in country’s economy.
Aim of industrial manufacturing:
To increase the production yield.
Adopting the technology driven techniques, where manual method
slow down the production speed.
Automatic fault detection and correction mechanism.
Minimize the production loss.
Develop proper supply chain.
ML in industrial manufacturing:
Learning models are used for optimization, control, and troubleshooting.
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12. Example:
Medical
Hospital, doctor and medical equipment collectively empower our health
infrastructure.
ML for medical diagnosis:
A simple disease can have multiple reasons. Ex-
Fever
Sex → male/female
Age
Frequency of occurrence
Intake of food supplement
Past history of some disease related to the respective patient, etc
Based on the above input, learning programs helps in medical diagnosis.
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13. References
E. Alpaydin, Introduction to machine learning. MIT press, 2020.
J. Grus, Data science from scratch: first principles with python. O’Reilly Media,
2019.
T. M. Mitchell, The discipline of machine learning. Carnegie Mellon University,
School of Computer Science, Machine Learning , 2006, vol. 9.
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