View source for Being-Creative-with-AI/C2/Machine-Learning-and-Deep-Learning/English

{|border=1

|-

|| '''Visual Cue'''

| '''Narration'''

|-

|| Slide 1
Title slide

Machine Learning and Deep Learning

| Welcome to this '''Spoken Tutorial''' on '''Machine Learning and Deep Learning'''.

|-

|| Slide 2

Learning Objectives

Montage of AI tools from previous tutorial (YouTube, Maps, Photos)

| In this '''tutorial''', we will learn

About '''Rule-based Programming''', '''Machine Learning''' and '''Deep Learning''', 

How '''Machine Learning''' and '''Deep Learning''' are connected to '''AI'''.

|-

|| Disclaimer Content Slide

| As AI tools constantly evolve, if you are unable to locate any icon or encounter difficulty at any step, you may use any conversational AI Chatbot for guidance.

|-

|| Slide 3
System Requirements

| Here I am using the following setup.

You do not need coding or techincal skills.

|-

|| Slide 4
Pre-requisite

http://EduPyramids.org

| For the Pre-requisites of this tutorial, visit the website shown on your screen.

|-

|| Image/animation: Show Animated gears or code flow

| Let's start with understanding '''rule-based programming''' using a real-life example.

|-

|| Image/animation: Automatic water dispenser with sensor

| Think about the time you used an automatic water tap.

You place your hand under a water tap.

The '''sensor''' in the tap detects your hand.

Then, it automatically turns on the water tap.

The water starts flowing.

|-

|| Image/animation: Close-up of tap sensor

Zoom in on sensor area; show "Detection = green light"

| The '''sensor''' follows one rule.

"If a hand is detected under the tap, then turn on the water tap."

|-

|| Image/animation: large vessel placed under the tap

Zoom in on sensor area; show "No Detection"

| Now, think about what would happen if we placed a large vessel under the tap?

The '''sensor''' in the tap will not detect it.

This happens because '''rules''' can't handle new cases.

They have limitations.

They can only follow the exact instructions we give them.

|-

|| Image/animation: Simple flowchart: Hand detected → Tap on → Water flows

| This is '''rule-based programming'''.

The '''machine''' simply follows the exact set of instructions given to it.

|-

|| Image/animation: "Machine Learning"

| Now, let's discuss '''Machine Learning''', commonly referred to as '''ML'''.

|-

|| Image/animation: Email inbox with spam and regular mails

| In the '''email inbox''' shown here, do you see a '''folder''' labelled '''Spam'''?

|-

|| Image/animation: Show Spam folder of an email

"Data → ML Model → Prediction"

Highlight the suspicious words, links, or senders.

Image/animation: Highlight spam mails vs regular mails (side by side)

Highlight "model" in the diagram

| Your unwanted '''email''' automatically goes into this '''folder'''.

This sorting happens using '''Machine Learning'''.

'''ML''' is trained using thousands of '''spam''' and regular '''emails'''.

From these examples, the '''machine''' learns patterns of what '''spam''' looks like.

It checks for things like suspicious words, strange '''links''', and unknown senders.

Once trained, it can predict whether a new '''email''' is '''spam''' or not.

|-

|| Pause icon overlay

| Pause the '''tutorial''' now.

Open the '''Spam folder''' in your '''email'''.

Look at the '''emails''' in this '''folder'''.

They have been separated from your '''inbox''' using '''Machine Learning'''.

|-

|| Transition slide: Deep Learning

| Now, let's go one step deeper into '''Machine Learning''' and understand '''Deep Learning'''.

'''Deep Learning''' is commonly referred to as '''DL'''.

|-

|| Visual: Voice assistant (e.g. Google Assistant)

what is the time in  '''India''' now

Show the response

| Let's open '''Google Chrome'''.

Click on the '''mic icon''' in the '''search bar''' to ask a question in '''voice mode'''.

Then ask, "What's the time in '''India''' now?"

It understands and processes the voice '''input''' and gives an answer.

This process happens with the help of '''Deep Learning'''.
|-

|| Visual: Illustration of neural network (dots and lines)

| Do you know how '''deep learning''' gave the answer?

|-

|| Visual: Illustration of neural network (dots and lines)

Animation: Small dots connected by lines

Small dots lighting up like decision nodes

Arrows between nodes passing signals

Layers lighting up one after another

"Many Layers = Deep Learning"

| '''Deep Learning''' uses '''artificial neural networks'''.

These '''networks''' have many layers that work together.

You can think of them as many tiny helpers.

Each helper understands a small part of the voice.

They pass this information from one layer to the next.

As information travels through the layers, the understanding becomes clearer.

Finally, the '''computer''' understands your question and gives the right answer.

|-

|| Visual: Layers lighting up one after another

"Many Layers = Deep Learning"

Show recognizing faces or understanding speech by computer

| As there are many layers, we refer to it as "'''deep'''."

Each layer helps improve the understanding a little more, like '''building blocks'''.

Hence, '''Deep Learning''' is used for complex tasks.

For example, in understanding speech or recognizing images.

|-

|| Visual: Russian doll animation: AI (outer) → ML (inner) → DL (innermost)

| Imagine '''AI''', '''ML''', and '''DL''' like ''' Russian'''  dolls.

One doll inside the other.

First comes '''AI''', the biggest doll.

Inside '''AI''' is '''ML''', and inside ''' ML''' is '''DL'''.

|-

|| Visual: Highlight outer doll: AI

| '''AI''' is a broad field that makes '''machines''' intelligent.

It's like the outer shell that includes many different methods.

|-

|| Visual: Highlight middle doll: ML

| '''ML''' is a '''subset''' of '''AI'''.

It learns from '''data'''.

It's a smaller doll inside '''AI'''.

|-

|| Visual: Highlight inner doll: DL

| '''Deep Learning''' is a '''subset''' of '''ML'''.

It is nested inside '''ML'''.

It learns from large amounts of '''data''' using '''artificial neural networks'''.

This lets it handle complex tasks.

|-

|| Summary slide:

Bulleted List

| Let's quickly summarize what we learnt in this tutorial.

We learnt that :

'''Rule-based programming''' follows fixed steps.

'''Machine Learning''' learns patterns from data.

'''Deep Learning''' uses artificial neural networks with many layers.

'''ML''' and '''DL''' are parts of '''AI'''.

|-

|| Assignment slide

Think of an '''application''' from your daily life that uses fixed rules.

Next, think of an '''application''' that learns from data or uses deep '''learning'''.

Write down both examples and explain why each one fits its category.

| Now, here's a quick assignment for you.

|-

|| Acknowledgement slides

| Thank you for joining.


|-

|| Final slide

This Spoken Tutorial is brought to you by 

'''EduPyramids Educational Services Private Limited''', SINE, IIT Bombay

| 

|-

|}