This document provides an overview of machine learning and different types of machine learning algorithms. It discusses how machine learning algorithms have been used to rank webpages, filter spam emails, and make predictions. The document explains that machine learning grew out of artificial intelligence research and allows computers to learn without being explicitly programmed. It describes supervised learning algorithms, which are taught using labeled training data, and unsupervised learning algorithms, which find hidden patterns in unlabeled data. Examples of supervised learning problems include regression to predict prices and classification to detect cancer. Unsupervised learning finds clusters within data without predefined labels.

1. Koteswar Rao Jerripothula
IGS(NM) PhD student
koteswar001@e.ntu.edu.sg

2. Machine Learning all around us
ML algorithms
have learnt how to
rank webpages

3. Spam folder
saving us from
tons of spams
through machine
learning
algorithms

4. • Machine Learning:
grew out of work in AI dream
giving new capability to computers
We can’t code every single thing, it is impossible. Machine
should be able to learn and how to make machine learn is
what we call machine learning algorithms
Still you have to code but to make machine learn.
Application: data mining, recommendation systems

5. Types
• 1) supervised learning algorithm: We teach
machine how to perform the task by providing
“right answers”
• 2) unsupervised learning algorithm: we let
machine learn by itself.

6. Supervised learning
• We teach by giving bunch of “right answers”
to some of the questions and ask algorithm to
produce more “right answers” to other
questions.
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Square ft
X100k $

7. What is the price for 22 square ft?
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So, if
machine can
learn the line
from the
training data
with real
answers
based on
that line it
can give real
answers for
other data.
This kind of
problem is
called
Regression
problem

8. Breast cancer or not?
This kind of problem
is called classification
problem
So, if machine can
learn the separator
from the training
data with real
answers based on
that separator it can
give real answers
for other data.
Age
Tumor size

9. • One may ask, a common man can also draw
these lines and predict.
• But common man cannot, when dimensions
increase.
• Just think if we start adding more features to
be able to predict more accurately.

10. Unsupervised Machine learning
Algorithms
Age
Tumor Size
Here goal is to
know the
structure of
data not
prediction
Different
clusters get
different labels

11. Thanks