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Learning Outcome
5
Analyze future challenges in AI systems
4
Connect pattern recognition with AI & real-world use
3
Explore major theories (Template, Prototype, Feature-based)
2
Identify how humans recognize patterns
1
Understand what pattern recognition is
Recall
"Think Before We Start"
How do you recognize a face instantly?
How do you identify spam messages?
How do you predict exam questions?
Analogy
“Your Brain is a Prediction Machine”
Human recognizing face
AI detecting object
“Your Brain is a Prediction Machine”
You see a face in 0.5 seconds.
AI scans 1000s of images to do the same.
Human recognizing face
AI detecting object
When you meet a friend in crowd -- No thinking, instant recognition.
Transition to Technical Concept
So What Is Pattern Recognition?
Input Brain Memory Decision
Pattern Recognition = Matching new information with stored memory
Definition:
Pattern Recognition means finding patterns or trends in data automatically using algorithms.
It helps machines understand data in areas like AI, images, speech, and time series.
Data Acquisition
Preprocessing
Feature Extraction
Classification
Post-processing
Sensor: collects raw data
Preprocessing: removes noise
Feature Extraction: selects important info
Classification: makes decision
Post-processing: improves result
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