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The model forms two main clusters:
• Cluster 1: Regular transactions that follow typical patterns.
• Cluster 2: Suspicious transactions that deviate significantly from normal behaviour.
Based on these clusters, the bank can flag transactions in Cluster 2 for further investigation, without needing prior
labels for fraudulent activity.
Regular Transactions
Suspicious Transactions
Total No. Total No. of 80
Transaction
of Regular Suspicious
ID 60
Transactions Transactions Unsupervised
Learning Transaction Frequency 40
Model 20
0
50 100 150 200 250
Transaction Amount ($)
This example illustrates how unsupervised learning helps uncover hidden patterns in unlabelled data, enabling
more effective decision-making.
Task 21 st Century #Technology Literacy
#Media Literacy
Skills
Identify the model: Supervised or Unsupervised?
Case 1: Email Classification
A system is trained to classify emails as either "Spam" or "Not Spam" based on labelled email data.
Case 2: Movie Recommendation System
An OTT streaming platform, groups users based on their viewing patterns and recommends movies without
predefined categories.
Case 3: Customer Segmentation
A retail company uses customer purchase histories to group customers into clusters based on buying behaviour,
with no predefined labels.
Case 4: Social media with tagged pictures
Social media platforms recognise your friend in a photo by analysing an album of tagged pictures.
Reinforcement Learning
Reinforcement Learning is a type of machine learning where a model learns through trial and error to make the
best decisions in a given environment. It interacts with its surroundings, receiving rewards for correct actions and
penalties for mistakes, gradually improving over time. The goal is to maximise cumulative rewards by learning
from experience and adapting to new situations.
For example, a robot learning to walk starts with random movements. When it takes a correct step, it gets a reward;
if it falls, it receives a penalty. Over time, the model learns which movements keep it balanced and eventually walks
efficiently.
Let us take an example of Reinforcement Learning:
You show the machine an image of a ball and ask it to predict
the object. Initially, the machine predicts it to be a globe. Since
Reinforcement Model Globe
this is incorrect, it receives negative feedback. The machine
then adjusts its understanding and learns that the object is not
a globe. When the same image is provided again, the machine
predicts it to be an orange. This is also incorrect, so it receives negative feedback again.
Advanced Concepts of Modeling in AI 123

