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1. Yo u r C o m p a n y N a m e
Artificial
Intelligence High
Technology
Power Point
Presentation Slides

2. Table of Content
2
Machine Learning
✓ 7 Steps of Machine learning
✓ Machine Learning vs. Traditional Programming
✓ How does machine learning work?
✓ Machine learning Algorithms
✓ Machine learning use cases
✓ How to choose Machine Learning Algorithm
✓ Why to use decision tree algorithm learning
✓ Challenges and Limitations of Machine learning
✓ Application of Machine learning
✓ Why is machine learning important?
Introduction
✓ What is AI?
✓ Introduction to AI Levels?
✓ Types of Artificial Intelligence
✓ AI VS machine learning vs deep learning
✓ Where is AI used?
✓ AI use cases
✓ Why is AI booming now?
✓ AI trend in 2020
Deep Learning
✓ What is Deep Learning?
✓ Deep learning Process
✓ Classification of Neural Networks
✓ Types of Deep Learning Networks
✓ Feed-forward neural networks
✓ Recurrent neural networks (RNNs)
✓ Convolutional neural networks (CNN)
✓ Reinforcement Learning
✓ Examples of deep learning applications
✓ Why is Deep Learning Important?
✓ Limitations of deep learning
Difference between AI vs ML vs DL
✓ What is AI?
✓ What is ML?
✓ What is Deep Learning?
✓ Machine Learning Process
✓ Deep Learning Process
✓ Difference between Machine Learning and
Deep Learning
✓ Which is better to start AI,ML or Deep learning
ARTIFICIAL
INTELLIGENCE

3. Table of Content
3
ARTIFICIAL
INTELLIGENCE
Unsupervised Machine Learning
✓ What is Unsupervised Learning?
✓ How Unsupervised Machine Learning works
✓ Types of Unsupervised Learning
✓ Disadvantages of Unsupervised Learning
Reinforcement Learning
✓ What is reinforcement learning?
✓ How reinforcement learning works
✓ Types of reinforcement learning
✓ Advantage of reinforcement learning
✓ Disadvantage of reinforcement learning
Supervised Machine Learning
✓ Types of Machine Learning
✓ What is Supervised Machine Learning?
✓ How Supervised Learning Works
✓ Types of Supervised Machine Learning Algorithms
✓ Supervised vs. Unsupervised Machine learning
techniques
✓ Advantages of Supervised Learning
✓ Disadvantages of Supervised Learning
Expert System in Artificial Intelligence
✓ What is an Expert System?
✓ Examples of Expert Systems
✓ Characteristic of Expert System
✓ Components of the expert system
✓ Conventional System vs. Expert system
✓ Human expert vs. expert system
✓ Benefits of expert systems
✓ Limitations of the expert system
✓ Applications of expert systems
Back Propagation Neural Network in AI
✓ Back Propagation Neural Network in AI
✓ What is Artificial Neural Networks?
✓ What is Backpropagation?
✓ Why We Need Backpropagation?
✓ What is a Feed Forward Network?
✓ Types of Backpropagation Networks
✓ Best practice Backpropagation

4. Introduction01
✓ What is AI?
✓ Introduction to AI Levels?
✓ Types of Artificial Intelligence
✓ AI VS machine learning vs deep learning
✓ Where is AI used?
✓ AI use cases
✓ Why is AI booming now?
✓ AI trend in 2020
4

5. Artificial Intelligence
Transforming the Nature of Work, Learning, and Learning to Work
5
Artificial intelligence (AI) is a popular branch of computer science that concerns with building “intelligent” smart
machines capable of performing intelligent tasks.
With rapid advancements in deep learning and machine learning, the tech industry is transforming radically.
Deep
Learning
Artificial
Intelligence
Machine
Learning

6. Introduction to AI Levels?
6
Types of
Artificial
Intelligence
Artificial Narrow Intelligence
Artificial General Intelligence
Artificial Super Intelligence

7. Types of Artificial Intelligence
7
Deep Learning
Machine Learning
Artificial Intelligence

8. Artificial Intelligence
8
2018
2019
2020
65%
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Artificial intelligence (AI) is a popular branch of computer science that concerns with building
“intelligent” smart machines capable of performing intelligent tasks.
With rapid advancements in deep learning and machine learning, tech industry is
transforming radically.

9. Machine Learning
9
This slide is 100% editable.
Adapt it to your needs and
capture your audience's
attention.
Machine learning is a type of AI that enables
machines to learn from data and deliver
predictive models.
The machine learning is not dependent on any
explicit programming but the data fed into it. It
is a complicated process.
Based on the data you feed into machine
learning algorithm and the training given to it,
an output is delivered.
A predictive algorithm will create a
predictive model.

10. Deep Learning
10
Deep Learning is a subfield of machine
learning that is concerned with
algorithms inspired by the brain's
structure & functions known as
artificial neural networks
A computer model can be taught
using Deep Learning to run
classification actions using pictures,
texts or sounds as input
01
02

11. AI VS Machine Learning VS Deep Learning
11
Artificial Intelligence
✓ Artificial Intelligence
originated around 1950s
✓ AI represents simulate
intelligence in machines
✓ AI is a subset of data
science
✓ Aim is to build machines
which are capable of
thinking like humans
Machine Learning
✓ Machine Learning originated
around 1960s
✓ Machine learning is the
practice of getting machines
to make decisions without
being programmed
✓ Machine learning is a
subset of AI & Data Science
✓ Aim is to make machines
learn through data so that
they can solve problems
Deep Learning
✓ Deep Learning originated
around 1970s
✓ Deep Learning is the
process of using artificial
neural networks to solve
complex problems
✓ Deep Learning is a subset of
Machine Learning, AI &
Data Science
✓ Aim is to build neural
networks that au6tonetically
discover patterns for feature
detection

12. Where is AI used?
12
Predictive
Analytics
Real-time
Operations Management
Customer
Services
Risk Management
& Analytics
Customer
Insight
Pricing &
Promotion
Customer
Experience
Supply
Chain
Human
Resources
Fraud
Detection
Knowledge
Creation
Research &
Development

13. AI Usecase in HealthCare
13
AI and
Robotics
Research
Training
Keeping Well
Early Detection
Diagnosis
Decision Making
Treatment
End of Life Care

14. AI Use Cases in Human Resource
14
Employee Life Cycle
Recruiting
✓ Dynamic Career Sites
✓ Smart Sourcing
Onboarding
✓ Automated Messages
✓ Curated Videos
Engagement
✓ HR Chatbot
✓ Engagement Surveys
Learning
✓ Curated Training
✓ Skill Development

15. AI in Banking for Fraud Detection
15
Authorization System
Neural
Network Engine
Scoring Engine
Case Creation Module
Case
Management
Database
Expert Authorization
Response Module
Expert Rules Base
Configuration
Workstation
Expert Rules Execute
Rules Definition
Cardholder Profiles
Postings
Payment System
Nonmonetary System
Analyst
Workstation
Auth Request1
3
Case Creation Rules Execute6
Payment and Non-Monetary Transactions8
Auth Recommendation4
Auth Request
& Score
2
Transaction & Score5
Case Information7

16. AI in Supply Chain
16
Procurement Manufacturing Customers ServiceLogistics
Pervasive Visibility
Proactive
Replenishment
Predictive
Maintenance
Secure Device
Maintenance
Ecosystem
Integration
Unified
Messaging
Actionable
Insights
IIOT – Securely Provisionally People, System and Things
Digital Ecosystem Data Lake
Secure Access Via Identity Management for Transient Users
Regulatory
Data
B2B
Transaction Data
Inventory
Data
Multimedia
Data
Sensor
Data
Logistics
Data
Trading
Partner
Data
Social
Media
Data
Structured & Unstructured
Information

17. Ai Chatbots in Healthcare
17
Search Engine
Users learn to search for information
Social Platforms
Like Facebook connect users online
Smartphones
Bring the internet online
App Eco-system
Lets users download and use apps easily
Messenger Apps
Lets users chat anywhere, anytime
Artificial Intelligence
Self Learning machines becomes smarter the more they are used
Healthbots
Bring all of the above together for healthcare use cases

18. Why is AI booming now?
18
100%
United
States
92%
China
84%
India
45%
Germany
54%
Israel
Penetration of Artificial Intelligence Skills, by Country
22%
24%
27%
36%
37%
51%
60%
60%
66%
71%
0 20 40 60 80 100
Fleet Mobile
Expediting Transactions
Production Floor Systems
Logistics & Supply Chain
Monitoring Through External Devices/Systems
Operational Environment
HR/Workforce Management
Security/Fraud
Customer Relation/Interaction (i.e., chatbots)
External Communication
Organizations deploying AI, by Functional Areas
8,097 7,540 7,336
4,680 4,201 3,714 3,655 3,564 3,169
0
2,000
4,000
6,000
8,000
Static Image
Recognition,
Classification and
Tagging
Algorithm Trading
Strategy Performance
Management
Efficient, Scalable
Processing of Patient
Data
Predictive
Maintenance
Object Identification,
detection,
classification, tracking
Text Query of Images Automated
Geophysical Feature
Detection
Content Distribution
on Social Media
Object detection &
classification,
avoidance, navigation
Global AI Revenue Forecast by 2025, Ranked by Use Case in millions US Dollar

19. 10 AI Trend in 2020
19
AI Trends”
“2020
Robotic
Process
Automation
AI will make
Healthcare more
Accurate
Data Modeling will
move to the Edge
AI will
Come for B2B
Ai-powered
Chatbots
Automated
Business Process
Advanced
Cybersecurity
AI Mediated Media
and Entertainment
Aerospace and
Flight Operations
Controlled by AI
AI In
Retail

20. Machine Learning02
✓ What is Machine Learning?
✓ 7 Steps of Machine learning
✓ Machine Learning vs. Traditional Programming
✓ How does machine learning work?
✓ Machine learning Algorithms
✓ Machine learning use cases
✓ How to choose Machine Learning Algorithm
✓ Why to use decision tree algorithm learning
✓ Challenges and Limitations of Machine learning
✓ Application of Machine learning
✓ Why is machine learning important?
20

21. Machine Learning
21
Traditional
Programming
Data
(Input)
Program
Output
Data
(Input)
Output
Machine
Learning
Program
“Learning”
Machine
Learning System
Input Data:
Feed Learner
Various Data
Output Data:
Present
Rules
Machine Learning is the result of General AI that involves developing
machines that can deliver results better than humans

22. 7 Steps of Machine Learning
22
01. Gathering Data 02. Preparing that Data
03. Choosing a Model
04. Training
05. Evaluation
06. Prediction
07. Hyperparameter Tuning

23. Machine Learning vs. Traditional Programming
23
Traditional Modelling
Prediction
Result
Machine Learning
Learning
Model
Prediction
Result
Computer
New Data
Model
Sample Data
Expected
Result
Data
Handcrafted
Model
Computer
Computer

24. How does Machine Learning Work?
24
Define
Objectives
Identify, the
problem to be
solved and create
a clear objective.
Collect Data
Collect data from
hospitals, health
insurance
companies, social
service agencies,
police and fire
dept.
Prepare Data
Preparing data is a
crucial step and
involves building
workflows to clean,
match and blend
the data.
Select
Algorithm
Depend on the
problem to be
solved and the
type of data an
appropriate
algorithm will be
chosen.
Train
Model
Data is fed as
input and the
algorithm
configured with
the required
parameters. A
percent of the data
can be utilized to
train the model.
Test
Model
The remaining
data is utilized to
test the model, for
accuracy.
Depending on the
results,
improvements, can
be performed in
the “Train model’
and/or “Select
Algorithm” phases,
iteratively.
Integrate
Model
Publish the
prepared
experiment as a
web service, so
applications can
use the model.

25. Machine Learning Algorithms
25
✓ Linear
✓ Polynomial
✓ KNN
✓ Trees
✓ Logistic Regression
✓ Naïve-Bayes
✓ SVM
Clustering
✓ SVD
✓ PCA
✓ K-means
✓ Apriori
✓ FP-Growth
Continuous
Categorical
Reinforcement
Machine Learning
Regression
Decision Tree
Classification
Association
Anlaysis
Hidden Markov
Model
Supervised Unsupervised
Random Forest

26. Machine Learning Use Cases
26
Energy Feedstock & Utilities
✓ Power Usage Analytics
✓ Seismic Data Processing
✓ Your Text Here
✓ Smart Grid Management
✓ Energy Demand & Supply Optimization
Healthcare & Life Sciences
✓ Alerts & Diagnostics from
Real-time Patient Data
✓ Your Text Here
✓ Predictive Health
Management
✓ Healthcare Provider
Sentiment Analysis
Financial Services
✓ Risk Analytics &
Regulation
✓ Customer
Segmentation
✓ Your Text Here
✓ Credit Worthiness
Evaluation
Travel & Hospitality
✓ Aircraft Scheduling
✓ Dynamic Pricing
✓ Your Text Here
✓ Traffic Patterns &
Congestion Management
Retail
✓ Predictive Inventory
Planning
✓ Recommendation
Engines
✓ Your Text Here
✓ Customer ROI &
Lifetime Value
Manufacturing
✓ Predictive Maintenance
or Condition Monitoring
✓ Your Text Here
✓ Demand Forecasting
✓ Process Optimization
✓ Telematics

27. How to Choose Machine Learning Algorithm
27
How to Select Machine Learning Algorithms
Accuracy
How to Select Machine Learning Algorithms
What do you want to do
with your Data?
Algorithm Cheat Sheet
Additional Requirements Training Time Linearity Number of
Parameters
Number of
Features

28. Why use Decision Tree Machine Learning Algorithm?
28
Decision Trees
To Classify
Non-linear Relationship
between Predictors &
Response
Linear Relationship
between Predictors
& Response
Use c4.5
Implementation
Use Standard
Regression Tree
Responsible
Variable has only
2 Categories
Response Variable
has Multiple
Categories
Use Standard
Classification here
Use c4.5
Implementation
To Predict
Responsible
variable is
Continuous

29. Challenges and Limitations of Machine learning
29
Data
Acquisition
High error-
Susceptibility
Interpretation
Results
Time and
Resources
DisadvantagesAdvantages
Easily Identifies Trends and Patterns
No Human Intervention needed
Continuous Improvement
Handling multi-dimensional & multi-variety Data
Wide Applications

30. Application of Machine Learning
30
Automatic Language
Translation
Medical
Diagnosis
Stock Market
Trading
Online Fraud
Detection
Virtual Personal
Assistant
Email Spam and
Malware Filtering
Self Driving
Cars
Product
Recommendations
Traffic
Prediction
Speech
Recognition
Image
Recognition

31. Why is Machine Learning Important?
31
Model
New Data Predicted DataPrediction
Phase 2: Prediction
Phase 1 : Learning
Training
Data
✓ Precision/recall
✓ Over fitting
✓ Test/cross Validation
data, etc.
Error Analysis
✓ Supervised
✓ Unsupervised
✓ Minimization, etc.
Learning
✓ Normalization
✓ Dimension Reduction
✓ Image Processing, etc.
Pre-Processing

32. Deep Learning03
✓ What is Deep Learning?
✓ Deep learning Process
✓ Classification of Neural Networks
✓ Types of Deep Learning Networks
✓ Feed-forward neural networks
✓ Recurrent neural networks (RNNs)
✓ Convolutional neural networks (CNN)
✓ Reinforcement Learning
✓ Examples of deep learning applications
✓ Why is Deep Learning Important?
✓ Limitations of deep learning
32

33. What is Deep Learning?
33
What is Deep Learning?
Input Feature Extraction + Classification Output
Car
Not Car
Deep Learning is a subfield of machine learning that is concerned with algorithms
inspired by the brain's structure and functions known as artificial neural networks.
A computer model can be taught using Deep Learning to run classification
actions using pictures, texts or sounds as input.

34. Deep Learning Process
34
Understand
the Problem
Identify
Data
Select Deep
Learning
Algorithms
Training
the Model
Test the
Model

35. Classification of Neural Networks
35
x1
x2
xn
v11
v12
vpn
w11
w22
wmp ym
y2
y1
1
2
p
1
2
m
V1n
w1p
Input Layer Hidden Layer Output Layer

36. Types of Deep Learning Networks
36
Deep Learning
Models
Supervised Unsupervised
✓ Artificial Neural Networks (ANN)
✓ Convolutional Neural Networks (CNN)
✓ Recurrent Neural Networks (RNN)
✓ Self Organizing Maps (SOM)
✓ Boltzmann Machines (BM)
✓ AutoEncoders (AE)
Supervised
Artificial Neural Networks Used for Regression & Classification
Convolutional Neural Networks Used for Computer Vision
Recurrent Neutral Networks Used for Time Series Analysis
Unsupervised
Self-Organizing Maps Used for Feature Detection
Deep Boltzmann Machines Used for Recommendation Systems
AutoEncoders Used for Recommendation Systems

37. Feed-forward Neural Networks
37
Input Layer Hidden Layer Output Layer
Variable- #1
Variable- #2
Variable- #3
Variable- # 4
Output

38. Recurrent Neural Networks (RNNs)
38
x1
x2
y
Input Layer
Recurrent Network
Output Layer
Hidden Layers

39. Convolutional Neural Networks (CNN)
39
Take Car = A1 Truck = B1 VAN = C1 Bicycle = D1 Rest all be Same

40. Reinforcement Learning
40
Reinforcement Learning
uses rewards and punishment to train
computing models to perform a sequence of
selections. Here computing faces a game-
like scenario where it employs trial and error to
answer. Based on the action it
performs, computing gets either rewards or
penalties. Its goal is to maximize the rewards.
Exploration Policy
Filters
Neural Networks
Memory
Algorithm
Action
State, Reward

41. Examples of Deep Learning Applications
41
Image Recognition
Natural Language Processing
Portfolio Management & Prediction of Stock Price Movements
Drug Discovery & Better Diagnostics of Diseases in Healthcare
Speech Recognition
Robots and Self - Driving Cars

42. Why is Deep Learning Important?
42
Deep Learning
Other Learning
Algorithms
Performance
Data

43. Limitations of Deep Learning
43
Limitations of
Deep Learning
Interpretability
Statistical Reasoning
Amount of Data

44. Difference between
AI vs ML vs DL
04
✓ What is Deep Learning?
✓ Deep learning Process
✓ Classification of Neural Networks
✓ Types of Deep Learning Networks
✓ Feed-forward neural networks
✓ Recurrent neural networks (RNNs)
✓ Convolutional neural networks (CNN)
✓ Reinforcement Learning
✓ Examples of deep learning applications
✓ Why is Deep Learning Important?
✓ Limitations of deep learning
44

45. Difference between AI vs ML vs DL
45
Engineering of making intelligent
machines and programs
Artificial Intelligence
Ability to learn without being
explicitly programmed
Machine Learning
Learning based on deep
neural network
Deep Learning

46. What is AI?
46
Artificial
Intelligence
(AI) is a popular branch of computer
science that concerns with building
“intelligent” smart machines capable of
performing intelligent tasks.
With rapid advancements in deep
learning and machine learning, the tech
industry is transforming radically.

47. What is ML?
47
Introduction to Machine learning
Ordinary
System
With
AI
Machine
Learning
Learns
Predicts
Improves
Machine Learning
is a type of AI that enables machines to learn from data and deliver predictive
models. The machine learning is not dependent on any explicit programming but the
data fed into it. It is a complicated process. Based on the data you feed into machine
learning algorithm and the training given to it, an output is delivered. A predictive
algorithm will create a predictive model.

48. What is Deep Learning?
48
Artificial intelligence (AI) is a popular branch
of computer science that concerns with
building “intelligent” smart machines capable
of performing intelligent tasks.
With rapid advancements in deep learning and
machine learning, the tech industry is
transforming radically.

49. Machine Learning Process
49
Data
Raw & Training Data
Visualization
Predictions & Strategy
Modelling
Candidate & Final
Data
Gathering
Data
Cleaning
Selecting
Right Algorithms
Building
Model & Finalizing
Data Transformation
into Predictions

50. Deep Learning Process
50
Understand
the Problem
Identify Data
Select Deep Learning
Algorithm
Training
the Model
Test
the Model

51. Difference between Machine Learning and Deep Learning
51
Machine Learning
Car
Not Car
Input Feature Extraction Classification Output
Input Feature Extraction + Classification Output
Deep Learning
Car
Not Car

52. Which is better to start AI,ML or DL?
52
Any Technique
which enables
computers to
mimic human
behavior.
Artificial
Intelligence
Subset of AI Techniques which use
Statistical Methods to Enable Machines
to Improve with Experiences.
Machine
Learning
Subset of ML
which make the
Computation of
Multi-layer Neural
Networks Feasible.
Deep
Learning

53. Supervised
Machine Learning
05
✓ Types of Machine Learning
✓ What is Supervised Machine Learning?
✓ How Supervised Learning Works
✓ Types of Supervised Machine Learning Algorithms
✓ Supervised vs. Unsupervised Machine learning
techniques
✓ Advantages of Supervised Learning:
✓ Disadvantages of Supervised Learning
53

54. Types of Machine Learning
54
Supervised
Learning
✓ Makes Machine Learn
Explicitly
✓ Data with Clearly
defined Output is given
✓ Direct feedback is given
✓ Predicts outcome/future
✓ Resolves Classification
and Regression
Problems
Inputs Outputs
Training
Inputs Outputs
Unsupervised
Learning
✓ Machine Understands
the data (Identifies
Patterns/ Structures)
✓ Evaluation is
Qualitative or Indirect
✓ Does not Predict/Find
anything Specific
Inputs Outputs
Rewards
Reinforcement
Learning
✓ An approach to AI
✓ Reward Based Learning
✓ Learning form +ve & +ve
Reinforcement
✓ Machine Learns how to
act in a Certain
Environment
✓ To Maximize Rewards

55. What is Supervised Machine Learning?
55
Supervised Learning
Input Raw Data
Output
Algorithm Processing
Training
Data set
Desired
Output
Supervisor

56. How Supervised Machine Learning works
56
Feed the Machine New, Unlabeled Information to See if it Tags New Data
Appropriately. If not, Continue Refining the Algorithm
Group 1
Group 2
Machine
Step 2
Label
“Group 1”
Machine
Step 1
Provide the Machine Learning Algorithm Categorized or “labeled”
Input and Output Data from to Learn
Classification
Sorting Items into Categories
Regression
Identifying Real Values
(Dollars, Weight, etc.)
Types of Problems to which it’s Suited

57. Types of Supervised Machine Learning Algorithms
57
Supervised
Learning
Classification Regression
✓ Fraud Detection
✓ Email Spam Detection
✓ Diagnostics
✓ Image Classification
✓ Risk Assessment
✓ Score Prediction

58. Supervised vs. Unsupervised Machine Learning Techniques
58
Supervised
Learning
Input & Output Data
Predictions &
Predictive Models
✓ Classification
✓ Regression
Predictions &
Predictive Models
Unsupervised
Learning
Input Data
✓ Clustering
✓ Association
VS

59. Advantages of Supervised Learning
59
It allows you to be very specific about the definition
of the labels. In other words, you'll train the
algorithm to differentiate different classes
where you'll set a perfect decision boundary.
You are ready to determine the
amount of classes you would
like to possess.
The input file is
extremely documented and
is labeled.
The results produced by the supervised
method are more accurate and reliable as
compared to the results produced by the
unsupervised techniques of machine
learning. this is often mainly because
the input file within the supervised
algorithm is documented and labeled. this is
often a key difference between supervised
and unsupervised learning.
The answers within the analysis and
therefore the output of your algorithm
are likely to be known thanks to that
each one the classes used are known.
Advantages

60. Disadvantages of Supervised Learning
60
✓ Supervised learning are often a posh method as compared with the unsupervised method.
The key reason is that you simply need to understand alright and label the inputs in
supervised learning.
✓ It doesn’t happen in real time while the unsupervised learning is about the important time. this
is often also a serious difference between supervised and unsupervised learning. Supervised
machine learning uses of-line analysis.
✓ It is needed tons of computation time for training.
✓ If you've got a dynamic big and growing data, you're unsure of the labels to predefine the principles. this will be a true challenge.

61. Unsupervised
Machine Learning
06
✓ What is Unsupervised Learning?
✓ How Unsupervised Machine Learning works
✓ Types of Unsupervised Learning
✓ Disadvantages of Unsupervised Learning
61

62. What is Unsupervised Learning?
62
Algorithm
Unsupervised Learning
Input Raw Data
Output
Interpretation Processing
o Unknown output
o No Training Data Set

63. How Unsupervised Machine Learning works
63
Clustering
Identifying similarities in groups
For Example: Are there patterns in the data to
indicate certain patients will respond better to
this treatment than others?
Anomaly Detection
Identifying abnormalities in data
For Example: Is a hacker intruding in
our network?
Step 1
Provide the machine learning algorithm uncategorized, unlabeled
input data to see what patterns it finds
Machine
Observe and learn from the patterns the machine identifies
Step 2
Machine
Similar Group 1
Similar Group 2
Types of Problems to Which it’s Suited

64. Types of Unsupervised Learning
64
Dimensionality Reduction Clustering
✓ Text Mining
✓ Face Recognition
✓ Big Data Visualization
✓ Image Recognition
✓ Biology
✓ City Planning
✓ Targeted Marketing
Unsupervised
Learning

65. Disadvantages of Unsupervised Learning
65
You cannot get very specific
about the definition of the
info sorting and therefore
the output. This is
often because the info utilized
in unsupervised learning is
labeled and not
known. It's employment of the
machine to label and group
the data before determining the
hidden patterns.
Less accuracy of the
results. This is
often also because
the input
file isn't known and
not labeled by
people beforehand ,
which suggests that
the machine will got
to do that alone.
The results of the
analysis can't
be ascertained. there's n
o prior knowledge within
the unsupervised
method of machine
learning. Additionally,
the numbers of
classes also are not
known. It results in the
lack to determine the
results generated by the
analysis.

66. Reinforcement
learning
07
✓ What is reinforcement learning?
✓ How reinforcement learning works
✓ Types of reinforcement learning
✓ Advantage of reinforcement learning
✓ Disadvantage of reinforcement learning
66

67. What is Reinforcement Learning?
67
Input Response Feedback Learns
It’s a
mango
Wrong!
It’s an
apple
Noted
It’s an
Apple
Reinforced
Response
Input

68. How Reinforcement Learning Works?
68
Reinforcement Learning
Environment
Agent
Input Raw Data
Output
Reward
State
Selection of
Algorithm
Best Action

69. Types of Reinforcement Learning
69
Gaming
Finance
Sector
Manufacturing
Inventory
Management
Robot
Navigation

70. Disadvantage of Reinforcement Learning
70
You cannot get very
specific about the
definition of the
info sorting and
therefore
the output. this is
often because the
info utilized
in unsupervised
learning is labeled
and not
known. it's employme
nt of the machine to
label and group
the data before
determining the
hidden patterns.
Less accuracy of the
results. this is often also
because the input
file isn't known and not
labeled by
people beforehand , which
suggests that the machine
will got to do that alone.
The results of the
analysis can't
be ascertained. There's no
prior knowledge within
the unsupervised method
of machine learning.
Additionally, the numbers
of classes also are not
known. It results in the
lack to determine the
results generated by the
analysis Reinforcement
learning as a framework is
wrong in many
various ways,
but it's precisely this
quality that creates it
useful.
Too much
reinforcement
learning can cause an
overload of
states which
may diminish the
results.
Reinforcement
learning isn't prefera
ble to use for solving
simple problems.
Reinforcement learning
needs tons of
knowledge and tons of
computation. it's data-
hungry. that's why it
works rather well in video
games because one can
play the sport again and
again and again, so
getting many data seems
feasible.

71. Back Propagation
Neural Network in AI
08
✓ Back Propagation Neural Network in AI
✓ What is Artificial Neural Networks?
✓ What is Backpropagation?
✓ Why We Need Backpropagation?
✓ What is a Feed Forward Network?
✓ Types of Backpropagation Networks
✓ Best practice Backpropagation
71

72. Back Propagation Neural Network in AI
72
1 2
i1
i2 h2
w1
b1 b2
net out

73. What is Artificial Neural Networks?
73
Feed-Forward
Network Output
Input Layer
Network Inputs
Hidden Layer
Back Propagation
Output Layer

74. What is Backpropagation Neural Networking?
74
x
x
x
w
w
w
w
Difference in
Desired Values
Backprop Output Layer
Input Layer
1
1
Hidden Layer(s)
3
Output Layer
5

75. Why We Need Backpropagation?
75
Most prominent
advantages of
Backpropagation Are:
Backpropagation
is fast, simple and
straightforward to
program.
It has no parameters
to tune aside from the
numbers of input.
It is a
versatile method because
it doesn't require prior
knowledge about the network.
It is a typical method
that generally
works well.
It doesn't need any special
mention of the features of the
function to
be learned.

76. What is a Feed Forward Network?
76
Input Layer
Hidden Layer
Output Layer

77. Types of Backpropagation Networks
77
• Static Back-propagation
• Recurrent Backpropagation
It is one quite backpropagation
network which produces a mapping of
a static input for static
output. it's useful to unravel static
classification issues like optical
character recognition.
Static Back-
propagation
Recurrent
Backpropagation
Recurrent backpropagation is
fed forward until a hard and
fast value is achieved. Then,
the error is computed and
propagated backward.
The main difference between both of those methods is: that the mapping is rapid in static back-
propagation while it's nonstatic in recurrent backpropagation

78. Best Practice Backpropagation
78
BACKPROPAGATION
A neural network is a group
of connected it I/O units
where each
connection features
a weight related to its
computer programs.
Backpropagation may be
a short form for
"backward propagation
of errors." it's a
typical method of
coaching artificial neural
networks.
Backpropagation
is fast, simple and
straightforward to
program.
A feedforward neural
network is a man-
made neural
network.

79. Expert System in
Artificial Intelligence
09
✓ What is an Expert System?
✓ Examples of Expert Systems
✓ Characteristic of Expert System
✓ Components of the expert system
✓ Conventional System vs. Expert system
✓ Human expert vs. expert system
✓ Benefits of expert systems
✓ Limitations of the expert system
✓ Applications of expert systems
79

80. Expert System in Artificial Intelligence
80
The Expert System in AI are computer applications. Also, with the assistance of this
development, we will solve complex problems. it's level of human intelligence and expertise
User
(May not be an expert)
Knowledge
Engineer
Human Expert
Knowledge
Base
Inference
Engine
User Interface

81. Examples of Expert Systems
81
Expert System
User
Interface
Knowledge
Base
Inference
Engine
Non-expert
User
Knowledge
from an expert
Query
Advice
The Highest
Level of Expertise
✓ The expert system offers the very
best level of experience. It provides
efficiency, accuracy and imaginative
problem-solving.
Right on
Time Reaction
✓ An Expert System interacts during
a very reasonable period of your
time with the user. the entire time
must be but the time taken by an
expert to urge the foremost accurate
solution for an equivalent problem.
Good
Reliability
✓ The expert system must be
reliable, and it must not make
any an error.
Flexible
✓ It is significant that it remains
flexible because it the is possessed
by an Expert system.
Effective Mechanism
✓ Expert System must have an
efficient mechanism to
administer the compilation
of the prevailing knowledge in it.
Capable of Handling
Challenging Decision &
Problems
✓ An expert system is capable of
handling challenging decision
problems and delivering solutions.

82. Characteristic of Expert System
82
✓ The system must be capable of
responding at A level of
competency adequate to or better than
an expert system within the field. the
standard of the recommendation given by
the system should be during a high level
integrity and that the performance ratio
should be also very high
High level Performance
✓ The expert system must be as
reliable as a person's expert
Good Reliability
✓ The system should be The system should be designed in
such how that it's ready to perform
within alittle amount of your time , like or better than the
time taken by a person's expert to succeed in at a
choice point. An expert system that takes a year to succeed
in a choice compared to a person's expert’s time of
1 hour wouldn't be useful
Adequate Response Time
✓ Expert systems are typically very domain specific. For ex., a
diagnostic expert system for troubleshooting computers
must actually perform all the required data manipulation
as a person's expert would. The developer of such a system
must limit his or her scope of the system to
only what's needed to unravel the target problem. Special
tools or programming languages are often needed to
accomplish the precise objectives of the system
Domain Specificity
✓ The system should be
understandable i.e. be ready
to explain the steps of reasoning
while executing. The expert system
should have an
evidence capability almost like the
reasoning ability of human experts
Understandable
✓ Expert systems use symbolic
representations for knowledge (rules,
networks or frames) and perform
their inference through symbolic
computations that closely resemble
manipulations of tongue
Use Symbolic
Representations

83. Components of the Expert System
83
Explanation
Experts and
Knowledge
Engineers
Inference Engine
Users
User
Interface
Acquisition Facility
Knowledge
Base

84. Conventional System vs. Expert System
84
01 01
02
03
04
0505
04
03
02
Knowledge
domain break away the
mechanism processing
The program could
have made an error
Not necessarily need
all the input/data
Changes within the rule are
often made with ease
The system can work only
with the rule as a tittle
Information and
processing combined
during a sequential file
The program
isn't wrong
Need all the
input file
Changes to the program
are inconvenient
The system works
if it's complete
vs

85. Human Expert vs. Expert System
85
(Artificial )
Expert
Systems
vs
Human
Experts
PermanentPerishable
Easy
to Transfer
Difficult
To Transfer
Easy
to Document
Difficult to
Document
Affordable, costly
to develop, but
cheap to operate
Expensive, especially
top notch
Add Your
Text Here
Add Your
Text Here

86. Benefits of Expert Systems
86
Easy to
Develop and
Modify the
System
Fast
Response
Low
Accessibility
Cost
Error Rate are
Very Low
Humans
Emotions are
not Affected

87. Limitations of the Expert System
87
Don’t Have Decision Making
Power Like Humans
Expert System is not Widely
used or Tested
Its Difficult to MaintainDevelopment Cost is High
There are Chances
of Errors
Not Able to Explain the
Logic Behind the Decision
It cant Deal with the Mixed
Knowledge
Its Developed for a
Specific Domain

88. Applications of Expert Systems
88
Design domain (Camera lens
design,automobile design)
Medical domain (Diagnosis
system, medical operations)
Monitoring
system
Process
Control System
Knowledge domain (Finding out the
faults in vehicles, computer)
Finance/Commerce (Stock market trading,
airline scheduling cargo scheduling)
Repairing
Warehousing
Optimization
Shipping

89. Artificial Intelligence High Technology PowerPoint Presentation Slides Icons Slide
89

90. Additional
Slides
A.I.
01 Stacked Column
02 Cluster Bar
03 Welcome to Our Agenda
04 Our Goal
05 Idea Generation
06 Venn
07 Timeline
08 Post It Notes
09 Thank You
90

91. Stacked Column
91
50%
60%
-15%
-22%
40%
50%
-19%
-2%
30%
36%
-10%
-20%
-30% -20% -10% 0% 10% 20% 30% 40% 50% 60% 70%
01
This graph/chart is linked to excel,
and changes automatically based
on data. Just left click on it and
select “Edit Data”.
Product
02
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and changes automatically based
on data. Just left click on it and
select “Edit Data”.
Product
03
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Product

92. Cluster Bar
92
01
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and changes automatically based
on data. Just left click on it and
select “Edit Data”.
Product
02
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select “Edit Data”.
Product
03
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select “Edit Data”.
Product
0 100 200 300 400 500 600 700 800
2015
2016
2017
2018
2019
2020

93. Welcome to Our Agenda
93
Agenda
9:00am-10:00am
01
Agenda
10:00am-11:00am
02
Agenda
11:00am-12:00pm
03
Agenda
01:00pm-02:00pm
04
Agenda
02:00pm-03:00pm
05
Agenda
03:00pm-04:00pm
06
Agenda
04:00pm-05:00pm
07
Agenda
05:00pm-06:00pm
08

94. Our Goal
94
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Goal 1
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95. Idea Generation
95
01
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96. Venn
96
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97. Timeline
97
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98. Post It Notes
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99. Thank You
99
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