1. Reinforcement Learning
in AI PowerPoint
Presentation
Slide Templates
Y o u r C o m p a n y N a m e

2. 2
Table of
Content
Machine Learning
• 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?
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
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

3. 3
Table of
Content
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
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
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

4. 4
Introduction
01
• 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

5. 5
Transforming the Nature of Work, Learning, and Learning to Work
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.
Artificial Intelligence
Deep Learning
Machine Learning
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Artificial Intelligence

6. Introduction to AI Levels?
6
Artificial Narrow
Intelligence
Artificial General
Intelligence
Artificial Super
Intelligence
Types of
Artificial Intelligence
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7. Types of Artificial Intelligence
7
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Machine
Learning
Artificial
Intelligence
Deep
Learning

8. Artificial Intelligence
8
2020
2019
2018
2020 2019 2018
AI
2016 2017 2018 2019 2020
AI
65% 35%
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.
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needs and capture your audience's attention
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needs and capture your audience's attention
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needs and capture your audience's attention
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needs and capture your audience's attention
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needs and capture your audience's attention

9. Machine Learning
9
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.
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10. Deep Learning
10
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Deep Learning is a subfield of machine learning that is
concerned with algorithms inspired by the brain's
structure
A computer model can be taught using Deep Learning
to run classification actions using pictures, texts or
sounds as input
functions known as artificial neural
networks
&

11. AI VS Machine Learning VS Deep Learning
11
• 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
Machine 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
Deep Learning
• 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
Artificial Intelligence
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12. Where is AI used?
12
Customer
Experience
Supply
Chain
Human
Resources
Knowledge
Creation
Research &
Development
Fraud
Detection
Real-time Operations
Management
Customer
Services
Risk Management &
Analytics
Customer
Insight
Pricing &
Promotion
Predictive
Analytics
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13. AI Usecase in HealthCare
13
AI and
Robotics
Research
Keeping Well
Early Detection
Diagnosis
Decision Making
Treatment
End of Life Care
Training
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14. AI Use Cases in Human Resource
14
LEARNING
• Curated Training
• Skill Development
RECRUITING
• Dynamic Career Sites
• Smart Sourcing
ENGAGEMENT
• HR Chatbot
• Engagement Surveys
ONBOARDING
• Automated Messages
• Curated Videos
Employee Life
Cycle
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15. AI in Banking for Fraud Detection
15
Configuration
Workstation
Expert Rules Execute
Rules Definition
Neural
Network
Engine Scoring
Engine
Authorization System
Case Creation Module
Case
Management Database
Expert Authorization
Response Module
Expert Rules Base
Cardholder Profiles
Postings
Payment System
Nonmonetary System
Analyst
Workstation
Auth Request
Case Creation Rules Execute
Payment and Non-Monetary
Transactions
Auth Recommendation
Auth Request
& Score
Transaction &
Score
Case
Information
08
01
02
05
06
07
04
03
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16. AI in Supply Chain
16
Logistics Service
Procurement Manufacturing Customers
Pervasive Visibility
Proactive
Replenishment
Predictive
Maintenance
IIOT – Securely Provisionally People, System and Things
Digital Ecosystem Data Lake
Secure Access Via Identity Management for Transient Users
Secure Device
Maintenance
Unified
Messaging
Actionable
Insights
Ecosystem
Integration
Structured & Unstructured
Information
Regulatory
Data
B2B Transaction
Data
Inventory
Data
Multimedia
Data
Sensor
Data
Logistics
Data
Trading
Partner Data
Social
Media Data
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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
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18. Why is AI booming now?
18
100% 92% 84%
45%
54%
United
States
China India
Germany
Israel
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
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
Penetration of Artificial Intelligence Skills, by Country Organizations deploying AI, by Functional Areas

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
AI In Retail
Aerospace and Flight
Operations
Controlled by AI
AI Mediated Media
and Entertainment
Advanced
Cybersecurity
Automated
Business Process
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20. 20
• 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?
Machine Learning
02

21. Machine Learning
21
Machine Learning is the result of General AI that involves developing
machines that can deliver results better than humans
Input Data:
Feed Learner
Various Data
Output Data:
Present Rules
“Learning”
Machine
Learning System
Traditional
Programming
Data
(Input)
Program
Output
Data
(Input)
Output
Machine
Learning
Program
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22. 7 Steps of Machine Learning
22
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Gathering Data
Preparing that Data
Choosing a Model
Training
Evaluation
Hyperparameter Tuning
Prediction

23. Machine Learning vs. Traditional Programming
23
Machine Learning
Learning Model
Sample Data
Expected
Result
Computer
Prediction Result
New Data
Model
Computer
Prediction Result
Computer
Data
Handcrafted
Model
Traditional Modelling
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24. How does Machine Learning Work?
24
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Identify, the problem to be
solved and create a clear
objective.
Define Objectives
Preparing data is a crucial
step and involves building
workflows to clean, match
and blend the data.
Prepare Data
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.
Train Model
Publish the prepared
experiment as a web
service, so applications can
use the model.
Integrate Model
Collect data from hospitals,
health insurance companies,
social service agencies,
police and fire dept.
Collect Data
Depend on the problem to
be solved and the type of
data an appropriate
algorithm will be chosen.
Select Algorithm
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.
Test Model

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

26. Machine Learning Use Cases
26
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Energy Feedstock
& Utilities
• Power Usage
Analytics
• Seismic Data
Processing
• Your Text Here
• Smart Grid
Management
• Energy Demand &
Supply Optimization
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
Manufacturing
• Predictive
Maintenance or
Condition
Monitoring
• Your Text Here
• Demand
Forecasting
• Process
Optimization
• Telematics
Retail
• Predictive
Inventory Planning
• Recommendation
Engines
• Your Text Here
• Customer ROI &
Lifetime Value
Healthcare &
Life Sciences
• Alerts & Diagnostics
from Real-time
Patient Data
• Your Text Here
• Predictive Health
Management
• Healthcare Provider
Sentiment Analysis

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

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

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

30. Application of Machine Learning
30
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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
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Training Data
• Normalization
• Dimension Reduction
• Image Processing, etc.
Pre-Processing
• Supervised
• Unsupervised
• Minimization, etc.
Learning
• Precision/recall
• Over fitting
• Test/cross Validation
data, etc.
Error Analysis
Phase 2: Prediction
Phase 1 : Learning
Model
New Data Predicted Data
Prediction

32. 32
• 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
Deep Learning
03

33. What is Deep Learning?
33
Input Feature Extraction + Classification Output
Car
Not Car
What is Deep Learning?
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.
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34. Deep Learning Process
34
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Understand
the Problem
Identify
Data Select Deep
Learning
Algorithms
Training
the Model Test the
Model

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

36. Types of Deep Learning Networks
36
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
Deep Learning
Models
• Artificial Neural Networks (ANN)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
Supervised
• Self Organizing Maps (SOM)
• Boltzmann Machines (BM)
• AutoEncoders (AE)
Unsupervised
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37. Feed-forward Neural Networks
37
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Input Layer Hidden Layer Output Layer
Variable- #1
Variable- #2
Variable- #3
Variable- # 4
Output

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

39. Convolutional Neural Networks (CNN)
39
Take Car = A1 Truck = B1 VAN = C1 Bicycle = D1 Rest all be Same
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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.
Action
State, Reward
Agent Environment
Exploration Policy
Filters
Algorithm
Neural Networks
Memory
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41. Examples of Deep Learning Applications
41
Image
Recognition
Portfolio
Management &
Prediction of Stock
Price Movements
Speech
Recognition
Natural Language
Processing
Drug Discovery & Better
Diagnostics of Diseases in
Healthcare
Robots and Self -
Driving Cars
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42. Why is Deep Learning Important?
42
Deep Learning
Other Learning Algorithms
Performance
Data
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43. Limitations of Deep Learning
43
Interpretability
Statistical Reasoning
Amount of Data
Limitations of Deep
Learning
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44. 44
• 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
Difference between AI vs ML vs DL
04

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
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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.
This slide is 100% editable. Adapt it to your needs and capture your audience's attention.

47. What is ML?
47
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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.
Learns
Predicts
Improves
Machine Learning
Ordinary System With AI
Introduction to Machine learning

48. What is Deep Learning?
48
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Deep Learning
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
Gathering
Building
Model & Finalising
Data Transformation
into Predictions
Data
Cleaning
Selecting
Right Algorithms
Modelling
Candidate & Final
Visualisation
Predictions & Strategy
Data
Raw & Training Data
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50. Deep Learning Process
50
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Understand
the Problem
Identify
Data
Select Deep
Learning Algorithm
Training the
Model
Test
the Model

51. Difference between Machine Learning and Deep Learning
51
Feature Extraction Classification Output
Car
Not Car
Input
Machine Learning
Deep Learning
Feature Extraction + Classification
Car
Not Car
Output
Input
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52. Which is better to start AI,ML or DL?
52
Subset of AI Techniques which use Statistical Methods
to Enable Machines to Improve with Experiences.
Machine Learning
Any Technique which enables computers to mimic
human behavior.
Artificial Intelligence
Subset of ML which make the Computation of Multi-
layer Neural Networks Feasible.
Deep Learning
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53. 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

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

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

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

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

58. Supervised vs. Unsupervised Machine Learning Techniques
58
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Supervised
Learning
• Classification
• Regression
Input & Output Data
Predictions &
Predictive Models
Unsupervised
Learning
• Clustering
• Association
Input Data
Patterns / Structure
Discovery
vs

59. Advantages of Supervised Learning
59
Advantages
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.
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60. Disadvantages of Supervised Learning
60
Disadvantages
• 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.
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61. 61
• What is Unsupervised Learning?
• How Unsupervised Machine Learning works
• Types of Unsupervised Learning
• Disadvantages of Unsupervised Learning
Unsupervised Machine Learning
06

62. What is Unsupervised Learning?
62
Unsupervised Learning
Processing
Interpretation
• Unknown output
• No Training Data Set
Input Raw Data Algorithm Output
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63. How Unsupervised Machine Learning works
63
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Provide the machine learning algorithm uncategorized,
unlabeled input data to see what patterns it finds
Observe and learn from the patterns the
machine identifies
Step 1 Step 2
Machine
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?
Types of Problems to Which it’s Suited
Similar Group 1
Similar Group 2
Machine

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

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

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

68. How Reinforcement Learning Works?
68
Reinforcement Learning
Reward
State
Selection of
Algorithm
Best Action
Agent
Environment Output
Input Raw Data
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69. Types of Reinforcement Learning
69
Gaming
Finance Sector Inventory Management
Manufacturing
Robot Navigation
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70. Disadvantage of Reinforcement Learning
70
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.
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.
Too much reinforcement learning can cause an overload of states which may diminish the results.
Reinforcement learning isn't preferable 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.
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71. Back Propagation Neural Network in AI
08
71
• 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

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

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

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

75. Why We Need Backpropagation?
75
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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 doesn't need any special mention of the
features of the function to
be learned.
It is a typical method that
generally works well.
Most prominent
advantages of
Backpropagation
Are:

76. What is a Feed Forward Network?
76
Input Layer
Hidden Layer
Output Layer
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77. Types of Backpropagation Networks
77
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
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 is fed forward until a
hard and fast value is achieved. Then, the error is
computed and propagated backward.
Recurrent Backpropagation
• Static Back-propagation
• Recurrent Backpropagation

78. Best Practice Backpropagation
78
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.
BACKPROPAGATION
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79. 79
• 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
Expert System in Artificial Intelligence
09

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
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User
(May not be an expert)
Knowledge
Engineer
Human
Expert
Knowledge
Base
Inference
Engine
User
Interface

81. Examples of Expert Systems
81
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.
Capable of Handling Challenging Decision & Problems
• An expert system is capable of handling challenging decision
problems and delivering solutions.
Effective Mechanism
• Expert System must have an efficient mechanism to
administer the compilation of the prevailing knowledge in it.
Expert System
User
Interface
Knowledge
Base
Inference
Engine
Non-expert
User
Knowledge from
an expert
Query
Advice

82. Characteristic of Expert System
82
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• 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
• 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
• 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
• 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 expert system must be as reliable as a
person's expert
Good Reliability
• 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

83. Components of the Expert System
83
Explanation
Inference Engine
Knowledge Base Acquisition Facility User Interface
Experts and
Knowledge
Engineers
Users
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84. Conventional System vs. Expert System
84
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
01
02
03
04
05 05
04
03
02
01
vs
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85. Human Expert vs. Expert System
85
Human Experts (Artificial ) Expert Systems
Add Your Text Here Add Your Text Here
Difficult to Transfer Easy to Transfer
Expensive, especially top notch Affordable, costly to develop, but cheap to operate
Difficult to Document Easy to Document
Perishable Permanent
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86. Benefits of Expert Systems
86
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Fast Response
Easy to Develop
and Modify the
System
Low Accessibility
Cost
Humans
Emotions are
not Affected
Error Rate are
Very Low

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

88. Applications of Expert Systems
88
Knowledge domain (Finding out the
faults in vehicles, computer)
Finance/Commerce (Stock market trading,
airline scheduling cargo scheduling)
Process Control System
Repairing
Monitoring system
Medical domain (Diagnosis
system, medical operations) Warehousing Optimization
Shipping
Design domain (Camera lens
design,automobile design)
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89. Reinforcement Learning in AI PowerPoint Presentation Slide Templates
Icons Slide
89

90. 90
Additional
Slides
Timeline
03
Target
04
Idea Generation
05
Venn
06
Post it notes
07
Thank you
08
Stacked Column
01
Area Chart
02

91. Stacked Column
91
4.3
2.5
-3.5
-4.5
2.4
4.4
-1.8
-0.8
-6
-4
-2
0
2
4
6
8
Sales
in
million
Product
02
Product
01
This graph/chart is linked to excel,
and changes automatically based on
data. Just left click on it and select
“Edit Data”.
Product 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

92. Area Chart
92
4.3
2.5
-3.5 -4.5
2.4
4.4
-1.8
-0.8
-5
-4
-3
-2
-1
0
1
2
3
4
5
Sales
in
million
Product 02
Product 01
This graph/chart is linked to excel,
and changes automatically based on
data. Just left click on it and select
“Edit Data”.
Product 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

93. Timeline
93
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2015 2016 2017 2018 2019 2020

94. Target
94
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Text Here
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Text Here
01
02
03

95. Idea Generation
95
02 04
01
03
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96. Venn
96
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Text Here Text Here Text Here Text Here Text Here

97. Post It Notes
97
01
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98. Thank You
98
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