Introduction to artifcial intelligence
Artificial intelligence (AI) is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen. 'Strong' AI is usually labelled as AGI (Artificial General Intelligence) while attempts to emulate 'natural' intelligence have been called ABI (Artificial Biological Intelligence). Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[3] Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving"
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What is artificial intelligence (IA) ?
1. WHAT IS ARTIFICIAL
INTELLIGENCE?
A I V S M A C H I N E L E A R N I N G V S D E E P L E A R N I N G
Name : Oussama BELAKHDAR
framed by :
Prof. Maria Moise, PhD
2. 01
Table of
Contents
Presentation Outline
Introduction to AI
Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Deep Learning
AI vs Machine Learning
vs Deep Learning
MACHINE LEARNING PRESENTATION
3. What is AI ?
Introduction to AI
Timeline : History of AI
Types of Artificial Intelligence
Artificiel Intelligence Value Chain Elemnts
Where is AI used ?
AI use cases
AI trend in 2020
Challenges in adoption of Artificial
Intelligence
02
Introduction
to
Artificial
Intelligence
4. What is Artificial Intelligence ?
Artifcial Intelligence is a popular branch of
computer science that concerns with
building "intelligent" smart machines
capable of performing intelligent tasks.
Artificial
Intelligence
With rapid advancements in deep learning
and machine learning, the tech industry is
transforming radically.
5. Sense Comprehend Act
Artificial Intelligence Introduction
AI
Technologies
Illustrative
Solutions
Natural
Language
Processing
Knowledge
Representation
Computer
Vision
Audio
Processing
Machine
Learning
Expert
Systems
Virtual
Agents
Identity
Analytics
Cognitive
Robotics
Speech
Analytics
Reommendation
Systems
Data
Visualization
6. Introduction to AI Levels ?
Artificial Narraw Intelligence
Artificial General Intelligence
Artificial Super Intelligence
Types of
Artigicial
Intelligence
8. Artificial Intelligence
Artificial Intelligence (AI) is a populare branch of compter science that
concerns with building "intelligent" smart machines capable of
performing intelligent tasks.
With rapid advancements in deep learning and machine learning, the
teck industry is transforming radically.
Artificial Intelligence
Transforming the Nature of Work, Learning, and Learning to Work
Machine Learning
Deep Learning
10. TIMELINE
The History of Artificial Intelligence
TURING TEST THE TERM AI ELIZA AI WINTER DEEP BLUE
SIRI WATSON TAY
ALEXA ALPHAGO ETHIC GUIDELINES
1950 1955 1966 1970s-1908s 1997
2011 2011 2014 2016 2017 2018
The Turning Test is a
method for determining the
intelligence of machine
The Term"Artificial
Intelligence" is used
for the first time
Eiza, one of the first
chatbots, simulates
conversations as a
psychotherapist
After the boom of
early esearch there
was a pause
Chess comptuer Deep
Blue wins againt chess
world champion Garri
kadparov
Siri, the intelligent
speech assitant, is
integratd into the
iPhone 4s for the
first time
Supercompter Watson
wins against two
human rivals in the
quiz show Jeopardy
Virtual assistant
Alexa from Amazon
moves into
countless homes
Chatbot, Tay had to be
reissued after a short
time because of racist
and sexist remarks
Google's AlphaGO
beats the world
champion in the
game Go
The European Union
establishes guidelines
for dealing with ethics
in AI
11. Types of Artificial Intelligence
Artificial Intelligence
Deep Learning Machine Learning
14. Artificial Intelligence Key Statistics
of senior
decision-makers agree that AI
is fundamental to the success
of their organisations strategy
, those currently or
planning to use AI technology
anticipate a 39% boost to their
organisation's revenue, on
average
organizations that
have replaced, or plan to
replace, roles with technology
will retain or redeploy those
who are displaced
Two-third
By 2022
7 in 10
17. Reasons of using Artificial Intelligence
Monitoring and alerts about
health and the business, 6.6%
Automation that eliminates
manual and repetitive tasks, 7.6%
All of hte above automated
data-driven reporting,18%
Automated Communications
that give consumers data they
can use to effective decisions,
12.8%
Automated data- driven
reporting, 5%
Automated communications
that give firms data they can
use to make effective
business decisions,45%
Other , 5%
18. Where is AI used ?
Customer Experience
Supply Chain
Human Resources
Fraud Detection
Knowledge Creation
Research & Development
Predictive Analytics
Real-time Operations Management
Customer Services
Risk Management & Analytics
Customer Insight
Pricing & Promotion
20. Core Areas of Artificial Intelligence
Explainable AI
Research on new algorithms
High precision learning from small
data sets
Sensory AI such as Internet of things
Physical AI like Industrial
Automation
Cognitive AI such as
worker training
General AI
22. ENGAGEMENT
HR Chatbot
Engagement
Surveys
Dynamic Career Sites
Smart Sourcing
AI Use Cases in Human Resource
LEARNING
Curated Training
Skill Development
RECRUITING
ONBOARDING
Automated Messages
Curate Videos
Employee Life Cycle
23. Potential Use Cases of AI in Healthcare
AI &
ROBOTICS
Training
Research
End of Life Care
Treatment
Decision Making
Keeping Well
Early Detection
Diagnosis
24. Bring all of the above
together for healthcare
use cases
Social Platforms Smartphones
AI Chatbots in Healthcare
Search Engine
Users Learn to search
for information
Like Facebook
connect users online
Bring the internet online
Healthbots
Self learning machines
becomes smarter the more
they are used
Messenger Apps
Lets users chat
anywhere, anytime
App Eco-system
Lets users dowload
and use apps easily
Artificial Intelligence
25. AI in Supply Chain
Digital Ecosystem Data Lake
Secure Access Via Identity Management for Transient Users
26. Payment System
Nonmonetary System
Authorization System
AI in Banking for Fraud Detection
Neural
Network Engine
Scoring Engine
Cardholder
Profiles
Postings
Case Creation Module
Case
Management
Database
Expert Rules Base
Expert Authorization
Response Module
Workstation
Configuration
Workstation
Analyst
27. Aerospace and Flight Oprations
Controlled by AI
10 AI Trend in 2020
AI In Retail
AI Mediated Media and Entertainent
Advanced Cybersecurity
Automated Business Process AI-powered Chatbots
AI will Come for B2B
Data Modling will move to the Edge
AI will make Healthcare more Accurate
Robotic Process Automation
28. Challenges in adoption of Artificial Intelligence
Lack of enabling data ecosystem
Low Intensity of AI research
Inadequate availability of expertise, technology & research
High resource cost & low awareness for adopting AI
in business process
Unclear privacy, security and ethical regulations
Unattrative intellectual Property regime to incentivize reseach &
adoption of AI
29. What is Machine Learning?
7 Steps of Machine Lrearning
Machine learning vs Traditional Programming
How does machine learning work?
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?
Types of Machine Learning
Machine
Learning
30. Machine Learning
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 create a predictive
model.
31. Machine Learning is the result of General AI that involves developing machines
that can deliver results bettr than humans
Machine Learning
Traditional
Programming
Machine
Learning
Data
(Input)
Data
(Input)
Program
Output
Output
Program
Input Data:
Feed Learner
Varios Data
Output Data:
Present Rules
"Learning"
Machine
Learning System
32. Is a type of that enables machines to learn from data and deliver predictive models. The machine
learning is not depedent on any explicit programming but the data fed into it. It is a complicated
process. Based on the data you feed into machine leaning algorithm and the training given to it, an
output is delivered. A predictive algoritthm will create a predictive model.
Improves
What is Machine Learning?
Machine Learning
Machine learning
Predicts
Introduction to Machine Learning
Learns
Ordinary System With Ai
34. INPUT DATA
information (+ Answers)
OUTPUT DATA
optimum Modl
Algorithms + Techniques
MACHINE LEARNING
Relationships Patterns
Hidden
Structures
Dependencies
35. Raw & Training Data
Selecting
Right Algorithms
Modelling
Machine Learning Process
Visualisation
Data
Gathering
Data
Cleaning
Building
Model & Finalising
Data Transformation
into Predictions
Candidate & Final
Prediction & Strategy
Data
36. Learning Error Analysis
Phase 2: Prediction
Why is Machine Learning Important?
Pre-processing
Phase 1 : Learning
Model
New Data Prediction Predicted Data
Normalisation
Dimension Reduction
Image Processing,etc.
Supervised
Unsupervised
Minimization, etc.
Precision/ recall
Over fitting
Test/cross Validation
data, etc.
Training
Data
37. Application of Machine Learning
Image Recognition
Speech Recognition
Traffic Prediction
Product Recommendations
Self Driving Cars Email Spam and Malware Filtering
Virtual Personal Assistant
Online Fraud Detection
Stock Market Trading
Medical DIagnosis
Automatic Language
Translation
38. MACHINE
LEARNING
USE CASES
Manufacturing
Financial
Services
Energy,
Feedstock &
Utilities
Seismic data
processing
Retail
Travel &
Hospitality
Healthcare &
Life Sciences
Smart grid
Management
Power
usage
analytics
Alerts &
diagnostics
from real-
time patient
data
Healthcare
provider
sentiment
analysis
Demand
forecasting
Predictive
maintenance
Predictive
invetory
planning
Reommenda
tion engines
Dynamic
pricing
Aircraft
Scheduling
Credit
worthiness
evaluation
Customer
segmentation
Traffic
patterns
Risk
analytics &
regulation
Process
optimization
Customer
Rol
Proactive
health
management
39. Machine Learning Use Cases
Energy Feedstock &
Utilities
Financial
Services
Travel &
Hospitality
Healthcare &
Life Sciences
Power Usage Analytics
Seismic Data
Processing
Smart Grid
Management
Energy Demand
Risk Analytics &
Regulation
Customer
Segmentation
Credit Worthiness
Evaluation
Aircraft Scheduling
Dynamic Pricing
Trafic Patteerns &
Congestion
Management
Predictive Maintenance
or Condition Monitoring
Demand Forecasting
Process Optimization
Telematics
Predictive
Inventory Planning
Recommendatio
Engines
Customer ROI &
Lifetime Value
Alerts & Diagnostics
from Real-time
Patient Data
Predictive Health
Management
Heathcare Provider
Manufacturing Retail
41. How does Machine Learning Work ?
Define Objectives Prepare Data Train model Integrate model
Collect Data Select Algorithm Test Model
Identifty, the problem
to be solved and create
a clear objective.
Preparing data is a crucial
step and involves building
workflows to clean, match
and blend the data
Data is fed as input and the
algorithm configured with
the required parameters. A
percent of th data can be
utilized to train the model.
Publish the prepared
expriment as web
sevice, so appliations
can use the model.
Collect data from hospitals,
health insurance
companies, social service
agencies, police and fire
dept.
Depend on the problem
to be solved and the
type of data an
appropriate algorithm
will be chosen.
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.
42. Machine Learning Process
STEP 1 STEP 2 STEP 3 STEP 4 STEP 5
Gathering data
from various
sources
Cleaning data
to have
homogeneity
Modeml Building
Selecting the right
ML algorithm
Gaining insights
from the model's
results
Data visualisation-
Transforming results
into visuals graphs
43. Additional
Requierements
How to Choose Machine learning Algorithm
How to Select Machine Learning Algorithms
Algorithm Cheat Sheet
What do you want
to
do with your Data?
Accuracy
Training
Time
Linearity
Number of
Parameters
Number of
Features
44. Machine Learning vs Traditional Programming
Computer
Computer
Data
Handcrafted
Model
Result
Sample Data
Expected
Result
Model
New Data
Model
Result
Traditional modelling
Machine Learning
Predition
Learning
Predition
Computer
45. Use c4.5
Implementation
To Classify
Why use Decision Tree Machine Learning Algorithm
Decision Trees
To Predict
Responsible
Variable has only
2 Categories
Response Variable
has
Multiple Categories
Use Standard
Classification
here
Use c4.5
Implementation
Responsible
variable is
Continuous
Linear Relationship
between Predictors
& Response
Non-linear Relationship
between Predictors &
Response
Use Standard
Regression Tree
46. Disadvantages
Challenges and Limitations of Machine Learning
Advantages
Easily Identifies Trends and Patterns
No Human Intervention Needed
Handling multi-dimensional &
multi-variety Data
Interpretation
Results
Continuos Improvement
Wide Applications
High Error-
Susceptibility
Time and
Resources
Data
Acquisition
47. Supervised
Learning
Unsupervised
Learning
Reinforcement
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 from +ve & +ve
Reinforcement
Machine Learns how to act in a
Certain Environment
To Maximise Rewards
Training Rewards
Types of Machine Learning
Data with Clearly define
Output is given
Resolves Classification and
Regression Problems
Makes Machine Learn Explicity
Direct feedback is given
Predict outome future
Inputs Inputs Inputs
Outputs Outputs Outputs
48. Hidden Markov
Model
Machine Learning Algorithms
Machine Learning
Supervised Unsupervised Reinforcement
Regression Clustering
Decision Tree
Random Forest
Classification
Association
Anlaysis
Linear
Polynomial
SVD
PCA
K-means
KNN
Trees
Logistic Regression
Naïve-Bayes
Apriori
FP-Growth
Continuous
Categorical
50. Types of Machine Learning
What is Supervised Machine Learning?
How Supervised Machine Works?
Types of Supervised Machine Learning Algorithms
Supervised vs Unspervised Machine Learning
Techniques
Advantages of Supervised Learning
Disadvantags of Supervised Learning
Supervised
Machine
Learning
53. How Supervised Machine Learning
works
Classification
Sorting Items into Categories
Regression
Identifing Real Values
(Dollars, Weight, etc.)
Types of Problems to which it's Suited
Feed the machine new, unlabeled information to
see if it Tags New data appropriately. If not,
Continue Refining the algorithm
Provide the Machine Learning Algorithm Categorized
or "labeled" Input and Output Data from to Learn
54. Advantages of Supervised Learning
Advantages
It allows to be very specific about the definition of the labels. In other words, you'll
train the algorithm to differntiate different classes where you'll set a perfect decision
boudary.
You are ready to determine the amount of classes you would like the possess.
The input file is extremely documented and is labeled
The results produced by the supervised method are more accurat and reliable as
compared to the results produced by the unsupervised techniques of machine
learning, This is often mainly because the imput file within the supervised algorithm
is documented an labeled. This is often a key difference between supervised and
unspervised learning.
The answers within the analysis and therefore the output of your algorithm are likely
to be know thanks to that each one classes used are known
55. 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
imput in supervised Learning.
It is needed tons of computation
time for training.
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
usupervised learning. Supervised
machine learning uses of-line
analysis.
If you're got a dynamic big and
growing data, you"re unsure of the
labels to predefine the principles.
This will be true challenge.
56. What is Unsupervised Learning?
How Unsupervised Machine Learning Works
Types of Unsupervised Learning
Disadvantages of Unsupervised Learning
Unsupervised
Machine
Learning
57. Algorithm Output
What is Unsupervised Learning?
Unsupervised Learning
Input Raw Data
Unkown output
No Training Data
Set
Interpretation Processing
58. How Unsupervised Machine Learning Works
Provide the machine learning algorith uncategorized,
unlabeled input data to see what patterns it finds
Observe and learn from the patterns
the machine identifies
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?
59. Types of Unsupervised Learning
Unsupervised
Learning
Dimensionality
Reduction
Text Mining
Face Recognition
Big Data
Visualization
Image Recognition
Clustering
Biology
City Planning
Targeted Marketing
60. 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 know. It's employement
of the machine to label and group the data before
determing 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 no know. It results in the
lack deterine the results generated by the analysis.
Less accuracy of the resuts. This is often also
because the imput file isn't known and not labeled
by peopme beforehand. which suggests that the
machine will got to do that alone.
Disadvantages of Unsupervised Learning
62. What is reinforcement learning?
How reinforcement learning works
Types of reinforcement learning
Advantages reinforcement learning
Disadvantage of reinforcement learning
Reinforcement
Learning
63. Reinforcement Learning
uses rewards and punishement 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 maximise the rewards.
Reinforcement Learning
Action
State, Reward
64. What is Reinforcement Learning?
Input Response Feedback Learns
Input
Reinforced
Response
It's a
mango
Wrong!
It's an
apple
Noted
It's an
apple
65. Unsupervised
Types of Deep Learning Netwoks
Artificial Neural Networks (ANN)
Convolutional Neural Networks
(CNN)
Recurrent Neural Networks (RNN)
Self Organinizing Maps
(SOM)
Bolzmann Machines (BM)
AutoEncoders (AE)
Artificial Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Used for Regresion & Classification
Used for Coputer Vision
Used for Time Series Analysis
Used for Feature Detection
Used for Recommendation Systems
Used for Recommendation Systems
Self-Oragnizin Maps
Deep Boltzmann Machines
AutoEncoders
Supervised
Supervised
Unsupervised
Deep Learning
Models
68. Disadvantage of Reinforcement Learning
You cannoy get very specific about the definition of the info sorting and therefore the
output. this is often because the info utilized in unspervised learning is labeled and
not know. it's employement of the machine to label and group the data before
determing the hidden patterns.
Less accuracy of the results. this is often also because the inpute file isn't know and not
labeled by peole 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
unsupervided method of machine learning. Additionally, the numbers of classes also are
not know. It results in the lack to determine the results genereted by the analysis
Reinforcement learning as a framework is wrong in many various ways, but it's precisly
this quality that creates it useful.
Too much reinforcement learning can cause an overload of states which may diminish
the results.
Reinforcement learning isn't prefereable to use for solving simple problems.
Reinforcement learning needs tons of knowledge and tons of compulation. 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.
69. What is Deep Learning?
Deep Learning Process
Classification of Neural Networks
Types of Deep Learning Networks
Reinfocement leanring
Examples of deep leaning applications
Why is Deep Learning Important ?
Limitations of Deep Learning
Deep Learning
70. Deep Learning
Deep Learning is a sublifield of machine learning that is
concerned with agorithms inspired by the brain's Structure
functions known as artificial neural
networks
A computer model can be taught using Deep Learning to
run classification actions using pictures, texts or sounds
as input
&
71. Feature Extraction + Classfication Output
Deep Learning is a subfield of machine learning that is
concerned with algorithms inspired by the brain's structure
and functions know as artificial neural networks
A computer model can be taught using Deep Learning to
run classification actions using pictures, texts or sounds as
inoput?
What is Deep Learning ?
What is Deep Learning?
Car
Not Car
Input
72. Artificial Intelligence (AI) is a popular
branch of computer science that concerns
with building "intelligent" smart machines
capable of performing intelligent tasks.
With rapid advancement in deep Learning
and machine learning, the tech industry is
transforming radically.
What is Deep Learning ?
76. Examples of Deep Learning Applications
Applications
of
Deep
Learning
Drug Discovery &
Better Diagnostics of
Diseases in Healthcare
Image
Recognition
Natural Language
Processing
Robots and Sell
Driving Cars
Speech
Recognition
Portfolio
Management &
Prediction of stock
Price Movements
77. Limitations of Deep Learning
Amount of Data
Interoretability
Statistical Reasoning
Limitations of
Deep Learning
78. Difference between Machine Learning
and Deep Learning
Which is better to start Artificiel
Intelligence , Machine Learning or Deep
Learning ?
Difference between
Artificial Intelligence
vs Machine Learning
vs Deep Learning
79. Artificial Intelligence originated
around 1950s
Machine Learning is the practice of
getting machines to make decisions
without being programmed
AI represents simulate intelligence
in machines
AI is a subset of data science Deep Learning is a subset of Machine
Learning Ai & Data Science
AI VS Machine Learning VS Deep Learning
Aim is to build machines which
are capable of thinking like
humans
Machine Learning originated around
1960s
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 originated around
1970s
Deep Learning is the process of using
artificial neural networks to solve
complex problems
Aim is to build neural network that
automatically discover patterns for
feature detection
Artificial Intelligence Machine Learning Deep Learning
80. Difference between Machine learning and deep learning
Feature Extraction
Feature Extraction+Classification
Classification
Imput Output
Imput Output
Machine Learning
Deep Learning
Car
Car
Not Car
Not Car
81. Difference between Artificial Intelligence
vs Machine Learning vs Deep Learning
Machine
Learning
Deep
Learning
Artificial
Intelligence
Engineering of
making intelligent
machines and
programs
Ability to learn
without
being explicity
programmed
Learning
based on deep
neural network
82. Which is better to start Artificiel Intelligence,
Machine Learning or Deep Learning?
Subset of AI Techniques which use statistical
Methods to Enable Machines to improve with
Experiences.
Subset of Machine Learning which make the
Computation of Multi-layer Neural Networks
Feasible.
Any technique which enables computers to mimic
human behavior
Artificial Intellignce
Machine Learing
Deep Learning
83. Machine learning algorithms learn from data to
find hidden relations, tomake predictions, to
interact with the world, …
A machine learning algorithm is as good as its input
data
• Good model + Bad data = Bad Results
Deep learning is making significant breakthroughs
in: speech recognition,language processing,
computer vision, control systems, …
If you are not using or considering using Deep
Learning to understand or solve vision problems,
you almost certainly should be
Conclusion
84. Bibliography
Books
1.
Introduction to Machine LearningThe Wikipedia Guide
Machine Learning for dummies A Willey Brand
MACHINE LEARNING Tom M Mitchell
Understanding Machine Learning: From Theory to
Algorithmsc 2014 by Shai Shalev-Shwartz and Shai Ben-David
2.Journals
Journal of Machine Learning Research www.jmlr.org
Machine Learning
IEEE Transactions on Neural Networks
IEEE Transactions on Pattern Analysis and Machine
Intelligence
Annals of Statistics
Journal of the American Statistical Association
International Conference on Machine Learning (ICML)
European Conference on Machine Learning (ECML)
Neural Information Processing Systems (NIPS)
Computational Learning
International Joint Conference on Artificial Intelligence (IJCAI)
ACM SIGKDD Conference on Knowledge Discovery and Data
Mining (KDD)
IEEE Int. Conf. on Data Mining (ICDM)
3.Conferences