AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck

AI vs ML vs DL
PowerPoint
Presentation
Slide Templates
Your Company Name
1

Table of Content
2
03. 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
02. 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?
04. 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
01. 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

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
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
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
3

➢ 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 Introduction
4

Artificial Intelligence
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.
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audience's attention.
Machine Learning
Deep Learning
5

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

Types of Artificial Intelligence
Deep Learning
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needs and capture your
Machine Learning
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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, the tech industry is transforming radically.
2018
2019
2020
2020 2019 2018
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65%
35%
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2016 2017 2018 2019 2020
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attention.
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Machine Learning
Information
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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Deep Learning
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

AI VS Machine Learning VS 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
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

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

AI Usecase in HealthCare
AI and Robotics
Research
Training
Keeping Well
Early DetectionDiagnosis
Decision Making
Treatment
End of Life Care

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

AI in Banking for Fraud Detection
Authorization System
Case Creation Module
Case
Management
Database
Expert Authorization Response
Module
Expert Rules Base
Configuration
Workstation
Rules Definition
Cardholder Profiles
Postings
Payment System
Nonmonetary System
Analyst
Workstation
Neural
Network Engine
Scoring Engine
6 Case Creation Rules Execute
Auth Request1
Expert Rules Execute3
Payment and
Non-Monetary Transactions
8
Auth Recommendation4
Auth Request & Score22
Transaction & Score5
Case Information7
Postings

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

Ai Chatbots in Healthcare
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?
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
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
United
States
100% 92%
China
84%
India
54%
Israel
45%
Germany

10 AI Trend in 2020
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
Aerospace and Flight Operations Controlled by AI
AI In Retail
Advanced Cybersecurity
AI Mediated Media and Entertainment

➢ 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?
02 Machine Learning
20

Machine Learning
Machine Learning is the result of General AI that involves
developing machines that can deliver results better than humans
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

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

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

How does Machine Learning Work?
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
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

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

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

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

Why use Decision Tree Machine Learning Algorithm?
To Classify
Responsible Variable
has only 2 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
Decision Trees
Response Variable has
Multiple Categories

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

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

➢ 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
03 Deep Learning
32

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.
Feature Extraction + ClassificationInput
Car
Not Car
Output

Deep Learning Process
Understand
the Problem
This slide is 100%
your needs and
capture your
Identify
Data
This slide is 100%
your needs and
capture your
Select Deep Learning
Algorithms
This slide is 100%
your needs and
capture your
Training
the Model
This slide is 100%
your needs and
capture your
Test the
Model
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your needs and
capture your
34

Classification of Neural Networks
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
35

Types of Deep Learning Networks
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
Supervised
o Artificial Neural Networks (ANN)
o Convolutional Neural Networks (CNN)
o Recurrent Neural Networks (RNN)
Unsupervised
o Self Organizing Maps (SOM)
o Boltzmann Machines (BM)
o AutoEncoders (AE)

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

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

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

FiltersExploration Policy
Memory
Neural Networks
Algorithm
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
State, Reward
Action
Agent
Environment

Examples of Deep Learning Applications
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?
Performance
Data
Deep Learning
Other Learning
Algorithms

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

➢ 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
04 Difference between AI vs ML vs DL
44

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

What is AI?
(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.
46

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

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.
48

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

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

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

Which is better to start AI,ML or DL?
Deep Learning
Machine Learning
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
52

05 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
53

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

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

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

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

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

Advantages of Supervised Learning
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
59

Disadvantages of Supervised Learning
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.
60

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

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

How Unsupervised Machine Learning works
Machine
Step1
Provide the machine learning algorithm
uncategorized, unlabeled input data to see
what patterns it finds
Step2
Observe and learn from the patterns the
machine identifies
Similar Group 1
Similar Group 2
Machine
Types of Problems to Which it’s Suited
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?
63

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

Disadvantages of Unsupervised Learning
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.
65

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

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

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

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

Disadvantage of Reinforcement Learning
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.
70

➢ 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
08 Back Propagation Neural Network in AI
71

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

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

What is Backpropagation Neural Networking?
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
74

Why We Need Backpropagation?MostprominentadvantagesofBackpropagationAre:
Backpropagation is fast, simple and straightforward to
program.
It has no parameters to tune aside from the numbers of
input.
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.
It is a versatile method because it doesn't require prior
knowledge about the network.
75

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

Types of Backpropagation Networks
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
o Static Back-propagation
o Recurrent Backpropagation
Recurrent backpropagation is fed forward until a
hard and fast value is achieved. Then, the error is
computed and propagated backward.
Recurrent Backpropagation
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
77

Best Practice BackpropagationBACKPROPAGATION
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.
78

➢ 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
09 Expert System in Artificial Intelligence
79

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

Examples of Expert Systems
The Highest Level of Expertise
o The expert system offers the very best level of experience. It
provides efficiency, accuracy and imaginative problem-solving.
Right on Time Reaction
o 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
o The expert system must be reliable, and it must not make
any an error.
Flexible
o It is significant that it remains flexible because it the is
possessed by an Expert system.
Effective Mechanism
o Expert System must have an efficient mechanism to
administer the compilation of the prevailing knowledge in it.
Capable of Handling Challenging Decision & Problems
o An expert system is capable of handling challenging decision
problems and delivering solutions.
Non-expert
User
Knowledge
from an expert
Inference
Engine
Knowledge
Base
User
Interface
Expert System
Query
Advice
81

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

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

Conventional System vs. Expert System
05
04
03
02
01
05
04
03
02
01 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
84

Human Expert vs. Expert System
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
85

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

Limitations of the Expert System
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
87

Applications of Expert Systems
8888
Knowledge domain (Finding out the faults in vehicles, computer)
Finance/Commerce (Stock market trading, airline scheduling cargo scheduling)
Repairing
Warehousing Optimization
Shipping
Medical domain
(Diagnosis system, medical operations)
Design domain
(Camera lens design,automobile design)
Process Control System
Monitoring system

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