Artificial Intelligence PowerPoint Presentation Slide Template Complete Deck

Artificial Intelligence
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Table of Content
2
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?
✓ 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
01
02
03
04

Table of Content
3
06
07
05
08
09
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
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
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

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

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

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

Types of Artificial Intelligence
7
Deep Learning
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it to your needs and capture your
audience's attention.
Machine Learning

8
65%
2018
2019
2020
35%
2018 2019 2020
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editable. Adapt it to your
needs and capture your
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AI
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2017 2018 2019 2020
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your needs and capture your
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AI
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Artificial intelligence (AI) is a popular branch of computer science that concerns with building “intelligent” smart machines capable of
With rapid advancements in deep learning and machine learning, tech industry is transforming radically.

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.

Deep Learning
10
A computer model can be taught using Deep Learning to run
classification actions using pictures, texts or sounds as input
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
&

AI VS Machine Learning VS Deep Learning
11
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
✓ 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

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

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

AI Use Cases in Human Resource
14
Employee Life
Cycle
LEARNING
✓ Curated Training
✓ Skill Development
RECRUITING
✓ Dynamic Career Sites
✓ Smart Sourcing
ONBOARDING
✓ Automated Messages
✓ Curated Videos
ENGAGEMENT
✓ HR Chatbot
✓ Engagement Surveys

AI in Banking for Fraud Detection
15
Analyst
Workstation
Configuration
Workstation
Rules Definition
Case Creation Rules
Execute
6
Cardholder
Profiles
Postings
Payment System
Nonmonetary System
Payment and Non-Monetary
Transactions
8
Expert Authorization
Response Module
Expert
Rules Base
Auth Recommendation4
Expert Rules Execute3
Neural
Network Engine
Scoring Engine
Case
Management
Database
Auth Request1
Auth Request & Score2
Transaction & Score5
Case Information7
Case Creation Module
Authorization System

AI in Supply Chain
16
Structured & Unstructured
Information
Regulatory
Data
B2B Transaction
Data
Inventory
Data
Multimedia
Data
Sensor
Data
Logistics
Data
Trading
Partner
Data
Social
Media
Data
Procurement Manufacturing Customers ServiceLogistics
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

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
Self Learning machines
becomes smarter the
more they are used
Healthbots
Bring all of the above
together for healthcare
use cases

Why is AI booming now?
18
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
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 AreasPenetration of Artificial Intelligence Skills, by Country
United
States
100%
China
92%
India
84%
Germany
45%
Israel
54%

10 AI Trend in 2020
19
AI Mediated Media and Entertainment
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
Advanced Cybersecurity
Automated Business Process

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?

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

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

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

How does Machine Learning Work?
24
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

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

Machine Learning Use Cases
26
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

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

Why use Decision Tree Machine Learning Algorithm?
28
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

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

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

Why is Machine Learning Important?
31
Model
New Data Predicted DataPrediction
Phase 2: Prediction
Phase 1 : Learning
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

32
Deep Learning03
✓ 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

What is Deep Learning?
33
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.
Car
Not Car
Input Feature Extraction + Classification Output

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

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
Classification of Neural Networks
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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
Types of Deep Learning Networks
36
✓ Self Organizing Maps (SOM)
✓ Boltzmann Machines (BM)
✓ Auto Encoders (AE)
✓ Artificial Neural Networks (ANN)
✓ Convolutional Neural Networks (CNN)
✓ Recurrent Neural Networks (RNN)
Supervised
Deep Learning
Models
Unsupervised

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

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

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

40
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 Neural Networks
Filters Memory
Algorithm
Agent Environment
Action
State, Reward

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

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

Limitations of Deep Learning
43
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to your needs and capture your
audience’s attention.
Interpretability
Amount of Data
Statistical Reasoning
Limitations
of Deep
Learning

44
Difference between AI vs ML vs DL04
✓ What is AI?
✓ What is ML?
✓ 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
45
Ability to learn
without being
explicitly programmed
Machine
Learning
Learning
based on deep
neural network
Deep
Learning
Engineering of making
intelligent machines
and programs
Artificial
Intelligence

What is AI?
46
(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.
A r t i f i c i a l
I n t e l l i g e n c e

What is ML?
47
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
Ordinary
System
With
AI
Machine
Learning
Introduction to Machine learning

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

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

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

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

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

53
Supervised Machine Learning05
✓ 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

Types of Machine Learning
54
Inputs Outputs
Unsupervised
Learning
Machine Understands the data
(Identifies Patterns/ Structures)
Evaluation is Qualitative or
Indirect
Does not Predict/Find anything
Specific
Rewards
Inputs Outputs
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
Training
Inputs Outputs
Supervised
Learning
Makes Machine Learn Explicitly
Data with Clearly defined Output is
given
Predicts outcome/future
Resolves Classification and
Regression Problems
Direct feedback is given

What is Supervised Machine Learning?
55
Training
Data set
Desired
Output
Supervisor
ProcessingAlgorithm
Input Raw Data Output
Supervised Learning

How Supervised Machine Learning works
56
Classification
Sorting Items into Categories
Regression
Identifying Real Values
(Dollars, Weight, etc.)
Types of Problems to which it’s Suited
Machine
Group 1
Group 2
Machine
Label
“Group 1”
Feed the Machine New, Unlabeled Information to See if it Tags New
Data Appropriately. If not, Continue Refining the Algorithm
Step2
Provide the Machine Learning Algorithm Categorized or
“labeled” Input and Output Data from to Learn
Step1

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

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

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

Disadvantages of Supervised Learning
60
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.
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.
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 Learning06
✓ What is Unsupervised Learning?
✓ How Unsupervised Machine Learning works
✓ Types of Unsupervised Learning
✓ Disadvantages of Unsupervised Learning

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

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?
Machine
Similar Group 1
Similar Group 2
Machine
Step 1
Provide the machine learning algorithm uncategorized, unlabeled
input data to see what patterns it finds
Step 2
Observe and learn from the patterns the machine identifies
Types of Problems to Which it’s Suited

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

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 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.

66
Reinforcement learning07
✓ What is reinforcement learning?
✓ How reinforcement learning works
✓ Types of reinforcement learning
✓ Advantage of reinforcement learning
✓ Disadvantage of reinforcement learning

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

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

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

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.

71
Back Propagation Neural Network in AI08
✓ 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

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

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

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

Why We Need Backpropagation?
75
It is a typical method that
generally
works well.
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.
Differentiators
Most prominent
advantages of
Backpropagation
Are:

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

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

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

79
Expert System in Artificial
Intelligence09
✓ 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
80
Knowledge
Base
Inference
Engine
User
Interface
User
(May not be an expert)
Human
Expert
Knowledge
Engineer
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

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.
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.
Query
Advice
Non-expert
User
Knowledge from
an expert
Expert System
Knowledge
Base
Inference
Engine
User
Interface

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
✓ 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
✓ 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 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

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

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

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

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

Limitations of the Expert System
87
Don’t Have Decision Making Power Like Humans
Its Developed for a Specific Domain
Its Difficult to Maintain
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

Knowledge domain (Finding out the faults in vehicles, computer)
Process Control System
Repairing
Monitoring system
Medical domain (Diagnosis
system, medical operations)
Shipping
Design domain (Camera lens
design,automobile design)
Applications of Expert Systems
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Bar Chart
91
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Clustered Column - Line
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93
Our
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Idea Generation
94
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96
2013 2015 2017 2019
2014 2016 2018 2020
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97
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Thank
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