Submit Search
Upload
What is Data Analysis and Machine Learning?
•
3 likes
•
332 views
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
Follow
An Introduction to Data Analysis and Machine Learning.
Read less
Read more
Engineering
Report
Share
Report
Share
1 of 48
Recommended
supervised learning
supervised learning
Amar Tripathi
Introduction to Deep Learning
Introduction to Deep Learning
Oswald Campesato
NVIDIA Story 2023.pdf
NVIDIA Story 2023.pdf
NVIDIA
An introduction to Deep Learning
An introduction to Deep Learning
Julien SIMON
Introduction to Deep learning
Introduction to Deep learning
leopauly
Machine Learning
Machine Learning
Vivek Garg
Image classification with Deep Neural Networks
Image classification with Deep Neural Networks
Yogendra Tamang
Machine Learning and Real-World Applications
Machine Learning and Real-World Applications
MachinePulse
Recommended
supervised learning
supervised learning
Amar Tripathi
Introduction to Deep Learning
Introduction to Deep Learning
Oswald Campesato
NVIDIA Story 2023.pdf
NVIDIA Story 2023.pdf
NVIDIA
An introduction to Deep Learning
An introduction to Deep Learning
Julien SIMON
Introduction to Deep learning
Introduction to Deep learning
leopauly
Machine Learning
Machine Learning
Vivek Garg
Image classification with Deep Neural Networks
Image classification with Deep Neural Networks
Yogendra Tamang
Machine Learning and Real-World Applications
Machine Learning and Real-World Applications
MachinePulse
Machine Learning - Object Detection and Classification
Machine Learning - Object Detection and Classification
Vikas Jain
Generative adversarial networks
Generative adversarial networks
Ding Li
Image segmentation with deep learning
Image segmentation with deep learning
Antonio Rueda-Toicen
AI vs Machine Learning vs Deep Learning | Machine Learning Training with Pyth...
AI vs Machine Learning vs Deep Learning | Machine Learning Training with Pyth...
Edureka!
Deep learning and Healthcare
Deep learning and Healthcare
Thomas da Silva Paula
Computer Vision
Computer Vision
ArtiKhanchandani
Computer vision and Open CV
Computer vision and Open CV
Chariza Pladin
Deep learning
Deep learning
Ratnakar Pandey
openCV with python
openCV with python
Wei-Wen Hsu
U-Net (1).pptx
U-Net (1).pptx
Changjin Lee
Machine Learning in Healthcare Diagnostics
Machine Learning in Healthcare Diagnostics
Larry Smarr
Deep Learning With Neural Networks
Deep Learning With Neural Networks
Aniket Maurya
Machine learning
Machine learning
Rajesh Chittampally
Machine learning seminar ppt
Machine learning seminar ppt
RAHUL DANGWAL
What is Deep Learning?
What is Deep Learning?
NVIDIA
Semantic Segmentation on Satellite Imagery
Semantic Segmentation on Satellite Imagery
RAHUL BHOJWANI
Artificial Intelligence, Machine Learning and Deep Learning
Artificial Intelligence, Machine Learning and Deep Learning
Sujit Pal
Deep Learning Explained
Deep Learning Explained
Melanie Swan
Neural networks and deep learning
Neural networks and deep learning
Jörgen Sandig
Introduction to Computer Vision using OpenCV
Introduction to Computer Vision using OpenCV
Dylan Seychell
The exciting new world of code & data
The exciting new world of code & data
Steven Miller
Issip nsf smart service systems 20170329 v1
Issip nsf smart service systems 20170329 v1
ISSIP
More Related Content
What's hot
Machine Learning - Object Detection and Classification
Machine Learning - Object Detection and Classification
Vikas Jain
Generative adversarial networks
Generative adversarial networks
Ding Li
Image segmentation with deep learning
Image segmentation with deep learning
Antonio Rueda-Toicen
AI vs Machine Learning vs Deep Learning | Machine Learning Training with Pyth...
AI vs Machine Learning vs Deep Learning | Machine Learning Training with Pyth...
Edureka!
Deep learning and Healthcare
Deep learning and Healthcare
Thomas da Silva Paula
Computer Vision
Computer Vision
ArtiKhanchandani
Computer vision and Open CV
Computer vision and Open CV
Chariza Pladin
Deep learning
Deep learning
Ratnakar Pandey
openCV with python
openCV with python
Wei-Wen Hsu
U-Net (1).pptx
U-Net (1).pptx
Changjin Lee
Machine Learning in Healthcare Diagnostics
Machine Learning in Healthcare Diagnostics
Larry Smarr
Deep Learning With Neural Networks
Deep Learning With Neural Networks
Aniket Maurya
Machine learning
Machine learning
Rajesh Chittampally
Machine learning seminar ppt
Machine learning seminar ppt
RAHUL DANGWAL
What is Deep Learning?
What is Deep Learning?
NVIDIA
Semantic Segmentation on Satellite Imagery
Semantic Segmentation on Satellite Imagery
RAHUL BHOJWANI
Artificial Intelligence, Machine Learning and Deep Learning
Artificial Intelligence, Machine Learning and Deep Learning
Sujit Pal
Deep Learning Explained
Deep Learning Explained
Melanie Swan
Neural networks and deep learning
Neural networks and deep learning
Jörgen Sandig
Introduction to Computer Vision using OpenCV
Introduction to Computer Vision using OpenCV
Dylan Seychell
What's hot
(20)
Machine Learning - Object Detection and Classification
Machine Learning - Object Detection and Classification
Generative adversarial networks
Generative adversarial networks
Image segmentation with deep learning
Image segmentation with deep learning
AI vs Machine Learning vs Deep Learning | Machine Learning Training with Pyth...
AI vs Machine Learning vs Deep Learning | Machine Learning Training with Pyth...
Deep learning and Healthcare
Deep learning and Healthcare
Computer Vision
Computer Vision
Computer vision and Open CV
Computer vision and Open CV
Deep learning
Deep learning
openCV with python
openCV with python
U-Net (1).pptx
U-Net (1).pptx
Machine Learning in Healthcare Diagnostics
Machine Learning in Healthcare Diagnostics
Deep Learning With Neural Networks
Deep Learning With Neural Networks
Machine learning
Machine learning
Machine learning seminar ppt
Machine learning seminar ppt
What is Deep Learning?
What is Deep Learning?
Semantic Segmentation on Satellite Imagery
Semantic Segmentation on Satellite Imagery
Artificial Intelligence, Machine Learning and Deep Learning
Artificial Intelligence, Machine Learning and Deep Learning
Deep Learning Explained
Deep Learning Explained
Neural networks and deep learning
Neural networks and deep learning
Introduction to Computer Vision using OpenCV
Introduction to Computer Vision using OpenCV
Similar to What is Data Analysis and Machine Learning?
The exciting new world of code & data
The exciting new world of code & data
Steven Miller
Issip nsf smart service systems 20170329 v1
Issip nsf smart service systems 20170329 v1
ISSIP
Machine Learning for Auditors: What you need to know - ISACA North America CA...
Machine Learning for Auditors: What you need to know - ISACA North America CA...
Andrew Clark
The Myths + Realities of Machine-Learning Cybersecurity
The Myths + Realities of Machine-Learning Cybersecurity
Interset
Breakout Session: Kritische Führungskompetenzen im Zeitalter der künstlichen ...
Breakout Session: Kritische Führungskompetenzen im Zeitalter der künstlichen ...
HWZ Hochschule für Wirtschaft
Journey to Industry 4.0 and Beyond with Cognitive Manufacturing -Taiwan compu...
Journey to Industry 4.0 and Beyond with Cognitive Manufacturing -Taiwan compu...
Cristene Gonzalez-Wertz
Machine_Learning_with_MATLAB_Seminar_Latest.pdf
Machine_Learning_with_MATLAB_Seminar_Latest.pdf
Carlos Paredes
Perspective on HPC-enabled AI
Perspective on HPC-enabled AI
inside-BigData.com
Where are the data professionals
Where are the data professionals
Steven Miller
Week 2 lecture
Week 2 lecture
RameshChandraPooniaC
Intelligently Automating Machine Learning, Artificial Intelligence, and Data ...
Intelligently Automating Machine Learning, Artificial Intelligence, and Data ...
Ali Alkan
An Elementary Introduction to Artificial Intelligence, Data Science and Machi...
An Elementary Introduction to Artificial Intelligence, Data Science and Machi...
Dozie Agbo
EXTENT-2017: Putting AI to Test
EXTENT-2017: Putting AI to Test
Iosif Itkin
AI-ML.pdf
AI-ML.pdf
AdiseshaK
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...
Ali Alkan
Machine learning
Machine learning
Siddharth Kar
Machine Learning for Auditors
Machine Learning for Auditors
Andrew Clark
H2O with Erin LeDell at Portland R User Group
H2O with Erin LeDell at Portland R User Group
Sri Ambati
Introduction to Machine Learning, Deep Learning and MXNet
Introduction to Machine Learning, Deep Learning and MXNet
Amazon Web Services
Overview of Machine Learning and its Applications
Overview of Machine Learning and its Applications
Deepak Chawla
Similar to What is Data Analysis and Machine Learning?
(20)
The exciting new world of code & data
The exciting new world of code & data
Issip nsf smart service systems 20170329 v1
Issip nsf smart service systems 20170329 v1
Machine Learning for Auditors: What you need to know - ISACA North America CA...
Machine Learning for Auditors: What you need to know - ISACA North America CA...
The Myths + Realities of Machine-Learning Cybersecurity
The Myths + Realities of Machine-Learning Cybersecurity
Breakout Session: Kritische Führungskompetenzen im Zeitalter der künstlichen ...
Breakout Session: Kritische Führungskompetenzen im Zeitalter der künstlichen ...
Journey to Industry 4.0 and Beyond with Cognitive Manufacturing -Taiwan compu...
Journey to Industry 4.0 and Beyond with Cognitive Manufacturing -Taiwan compu...
Machine_Learning_with_MATLAB_Seminar_Latest.pdf
Machine_Learning_with_MATLAB_Seminar_Latest.pdf
Perspective on HPC-enabled AI
Perspective on HPC-enabled AI
Where are the data professionals
Where are the data professionals
Week 2 lecture
Week 2 lecture
Intelligently Automating Machine Learning, Artificial Intelligence, and Data ...
Intelligently Automating Machine Learning, Artificial Intelligence, and Data ...
An Elementary Introduction to Artificial Intelligence, Data Science and Machi...
An Elementary Introduction to Artificial Intelligence, Data Science and Machi...
EXTENT-2017: Putting AI to Test
EXTENT-2017: Putting AI to Test
AI-ML.pdf
AI-ML.pdf
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...
Machine learning
Machine learning
Machine Learning for Auditors
Machine Learning for Auditors
H2O with Erin LeDell at Portland R User Group
H2O with Erin LeDell at Portland R User Group
Introduction to Machine Learning, Deep Learning and MXNet
Introduction to Machine Learning, Deep Learning and MXNet
Overview of Machine Learning and its Applications
Overview of Machine Learning and its Applications
More from Arindam Chakraborty, Ph.D., P.E. (CA, TX)
FEA Based Level 3 Assessment of Deformed Tanks with Fluid Induced Loads
FEA Based Level 3 Assessment of Deformed Tanks with Fluid Induced Loads
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
Navigating Complexity: The Role of Trusted Partners and VIAS3D in Dassault Sy...
Navigating Complexity: The Role of Trusted Partners and VIAS3D in Dassault Sy...
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
Shelf-Life Prediction for Consumer Packaged Goods (CPG) Bottles
Shelf-Life Prediction for Consumer Packaged Goods (CPG) Bottles
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
Ensuring Structural Compliance of Electric Vehicle Battery Pack Against Crush...
Ensuring Structural Compliance of Electric Vehicle Battery Pack Against Crush...
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
Digital Twin based Product Development in Life Science Industry – Sustainable...
Digital Twin based Product Development in Life Science Industry – Sustainable...
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
Engineered to Cure – Patient Specific Tibial Implant Design using Micro-Macro...
Engineered to Cure – Patient Specific Tibial Implant Design using Micro-Macro...
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
FEA Based Level 3 Assessment of Deformed Tanks
FEA Based Level 3 Assessment of Deformed Tanks
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
Structural Compliance of Electric Vehicle Battery Pack
Structural Compliance of Electric Vehicle Battery Pack
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
Integrating Laser Scan Data into FEA Model to Perform Level 3 FFS
Integrating Laser Scan Data into FEA Model to Perform Level 3 FFS
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
ORS 2022-Tibial implant analysis using patient specific data
ORS 2022-Tibial implant analysis using patient specific data
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
Electromagnetic Simulations for Aerospace Applications
Electromagnetic Simulations for Aerospace Applications
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
Simulation Study of Brake System Performance
Simulation Study of Brake System Performance
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
Fracture Reliability
Fracture Reliability
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
Reliability Based Design Optimization of Primary Shield Structure Under Seism...
Reliability Based Design Optimization of Primary Shield Structure Under Seism...
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
Analysis of ERDIP joint at fault using Abaqus - A case study for simulation b...
Analysis of ERDIP joint at fault using Abaqus - A case study for simulation b...
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
CFD simulation Capabilities for marine / offshore Applications
CFD simulation Capabilities for marine / offshore Applications
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
FEA Based Pipe Stress Analysis - Introduction To Pipe Calculation System (PCS)
FEA Based Pipe Stress Analysis - Introduction To Pipe Calculation System (PCS)
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
Vias services and capabilities
Vias services and capabilities
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
Simulation in the CPG-retail Industry
Simulation in the CPG-retail Industry
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
Using MpCCI to model Fluid-Structure-Interactions with ABAQUS and 3rd party C...
Using MpCCI to model Fluid-Structure-Interactions with ABAQUS and 3rd party C...
Arindam Chakraborty, Ph.D., P.E. (CA, TX)
More from Arindam Chakraborty, Ph.D., P.E. (CA, TX)
(20)
FEA Based Level 3 Assessment of Deformed Tanks with Fluid Induced Loads
FEA Based Level 3 Assessment of Deformed Tanks with Fluid Induced Loads
Navigating Complexity: The Role of Trusted Partners and VIAS3D in Dassault Sy...
Navigating Complexity: The Role of Trusted Partners and VIAS3D in Dassault Sy...
Shelf-Life Prediction for Consumer Packaged Goods (CPG) Bottles
Shelf-Life Prediction for Consumer Packaged Goods (CPG) Bottles
Ensuring Structural Compliance of Electric Vehicle Battery Pack Against Crush...
Ensuring Structural Compliance of Electric Vehicle Battery Pack Against Crush...
Digital Twin based Product Development in Life Science Industry – Sustainable...
Digital Twin based Product Development in Life Science Industry – Sustainable...
Engineered to Cure – Patient Specific Tibial Implant Design using Micro-Macro...
Engineered to Cure – Patient Specific Tibial Implant Design using Micro-Macro...
FEA Based Level 3 Assessment of Deformed Tanks
FEA Based Level 3 Assessment of Deformed Tanks
Structural Compliance of Electric Vehicle Battery Pack
Structural Compliance of Electric Vehicle Battery Pack
Integrating Laser Scan Data into FEA Model to Perform Level 3 FFS
Integrating Laser Scan Data into FEA Model to Perform Level 3 FFS
ORS 2022-Tibial implant analysis using patient specific data
ORS 2022-Tibial implant analysis using patient specific data
Electromagnetic Simulations for Aerospace Applications
Electromagnetic Simulations for Aerospace Applications
Simulation Study of Brake System Performance
Simulation Study of Brake System Performance
Fracture Reliability
Fracture Reliability
Reliability Based Design Optimization of Primary Shield Structure Under Seism...
Reliability Based Design Optimization of Primary Shield Structure Under Seism...
Analysis of ERDIP joint at fault using Abaqus - A case study for simulation b...
Analysis of ERDIP joint at fault using Abaqus - A case study for simulation b...
CFD simulation Capabilities for marine / offshore Applications
CFD simulation Capabilities for marine / offshore Applications
FEA Based Pipe Stress Analysis - Introduction To Pipe Calculation System (PCS)
FEA Based Pipe Stress Analysis - Introduction To Pipe Calculation System (PCS)
Vias services and capabilities
Vias services and capabilities
Simulation in the CPG-retail Industry
Simulation in the CPG-retail Industry
Using MpCCI to model Fluid-Structure-Interactions with ABAQUS and 3rd party C...
Using MpCCI to model Fluid-Structure-Interactions with ABAQUS and 3rd party C...
Recently uploaded
Online food ordering system project report.pdf
Online food ordering system project report.pdf
Kamal Acharya
Orlando’s Arnold Palmer Hospital Layout Strategy-1.pptx
Orlando’s Arnold Palmer Hospital Layout Strategy-1.pptx
MuhammadAsimMuhammad6
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
ssuser89054b
Electromagnetic relays used for power system .pptx
Electromagnetic relays used for power system .pptx
NANDHAKUMARA10
💚Trustworthy Call Girls Pune Call Girls Service Just Call 🍑👄6378878445 🍑👄 Top...
💚Trustworthy Call Girls Pune Call Girls Service Just Call 🍑👄6378878445 🍑👄 Top...
vershagrag
Thermal Engineering Unit - I & II . ppt
Thermal Engineering Unit - I & II . ppt
DineshKumar4165
Introduction to Serverless with AWS Lambda
Introduction to Serverless with AWS Lambda
Omar Fathy
AIRCANVAS[1].pdf mini project for btech students
AIRCANVAS[1].pdf mini project for btech students
vanyagupta248
Max. shear stress theory-Maximum Shear Stress Theory Maximum Distortional ...
Max. shear stress theory-Maximum Shear Stress Theory Maximum Distortional ...
ronahami
Basic Electronics for diploma students as per technical education Kerala Syll...
Basic Electronics for diploma students as per technical education Kerala Syll...
ppkakm
Employee leave management system project.
Employee leave management system project.
Kamal Acharya
Introduction to Data Visualization,Matplotlib.pdf
Introduction to Data Visualization,Matplotlib.pdf
sumitt6_25730773
Hostel management system project report..pdf
Hostel management system project report..pdf
Kamal Acharya
HOA1&2 - Module 3 - PREHISTORCI ARCHITECTURE OF KERALA.pptx
HOA1&2 - Module 3 - PREHISTORCI ARCHITECTURE OF KERALA.pptx
SCMS School of Architecture
Bhubaneswar🌹Call Girls Bhubaneswar ❤Komal 9777949614 💟 Full Trusted CALL GIRL...
Bhubaneswar🌹Call Girls Bhubaneswar ❤Komal 9777949614 💟 Full Trusted CALL GIRL...
Call Girls Mumbai
Digital Communication Essentials: DPCM, DM, and ADM .pptx
Digital Communication Essentials: DPCM, DM, and ADM .pptx
pritamlangde
NO1 Top No1 Amil Baba In Azad Kashmir, Kashmir Black Magic Specialist Expert ...
NO1 Top No1 Amil Baba In Azad Kashmir, Kashmir Black Magic Specialist Expert ...
Amil baba
Ghuma $ Russian Call Girls Ahmedabad ₹7.5k Pick Up & Drop With Cash Payment 8...
Ghuma $ Russian Call Girls Ahmedabad ₹7.5k Pick Up & Drop With Cash Payment 8...
gragchanchal546
457503602-5-Gas-Well-Testing-and-Analysis-pptx.pptx
457503602-5-Gas-Well-Testing-and-Analysis-pptx.pptx
rouholahahmadi9876
Design For Accessibility: Getting it right from the start
Design For Accessibility: Getting it right from the start
Quintin Balsdon
Recently uploaded
(20)
Online food ordering system project report.pdf
Online food ordering system project report.pdf
Orlando’s Arnold Palmer Hospital Layout Strategy-1.pptx
Orlando’s Arnold Palmer Hospital Layout Strategy-1.pptx
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
Electromagnetic relays used for power system .pptx
Electromagnetic relays used for power system .pptx
💚Trustworthy Call Girls Pune Call Girls Service Just Call 🍑👄6378878445 🍑👄 Top...
💚Trustworthy Call Girls Pune Call Girls Service Just Call 🍑👄6378878445 🍑👄 Top...
Thermal Engineering Unit - I & II . ppt
Thermal Engineering Unit - I & II . ppt
Introduction to Serverless with AWS Lambda
Introduction to Serverless with AWS Lambda
AIRCANVAS[1].pdf mini project for btech students
AIRCANVAS[1].pdf mini project for btech students
Max. shear stress theory-Maximum Shear Stress Theory Maximum Distortional ...
Max. shear stress theory-Maximum Shear Stress Theory Maximum Distortional ...
Basic Electronics for diploma students as per technical education Kerala Syll...
Basic Electronics for diploma students as per technical education Kerala Syll...
Employee leave management system project.
Employee leave management system project.
Introduction to Data Visualization,Matplotlib.pdf
Introduction to Data Visualization,Matplotlib.pdf
Hostel management system project report..pdf
Hostel management system project report..pdf
HOA1&2 - Module 3 - PREHISTORCI ARCHITECTURE OF KERALA.pptx
HOA1&2 - Module 3 - PREHISTORCI ARCHITECTURE OF KERALA.pptx
Bhubaneswar🌹Call Girls Bhubaneswar ❤Komal 9777949614 💟 Full Trusted CALL GIRL...
Bhubaneswar🌹Call Girls Bhubaneswar ❤Komal 9777949614 💟 Full Trusted CALL GIRL...
Digital Communication Essentials: DPCM, DM, and ADM .pptx
Digital Communication Essentials: DPCM, DM, and ADM .pptx
NO1 Top No1 Amil Baba In Azad Kashmir, Kashmir Black Magic Specialist Expert ...
NO1 Top No1 Amil Baba In Azad Kashmir, Kashmir Black Magic Specialist Expert ...
Ghuma $ Russian Call Girls Ahmedabad ₹7.5k Pick Up & Drop With Cash Payment 8...
Ghuma $ Russian Call Girls Ahmedabad ₹7.5k Pick Up & Drop With Cash Payment 8...
457503602-5-Gas-Well-Testing-and-Analysis-pptx.pptx
457503602-5-Gas-Well-Testing-and-Analysis-pptx.pptx
Design For Accessibility: Getting it right from the start
Design For Accessibility: Getting it right from the start
What is Data Analysis and Machine Learning?
1.
WHAT IS DATA
ANALYTICS AND MACHINE LEARNING? July 12, 2017 Arindam Chakroborty, PhD, PE Burak Ozturk, PhD, CEng Ricardo Vilalta, PhD 1400 Broadfield Blvd. Suite 325, Houston TX 77084 Phone : +1 (832) 301-0881 www.viascorp.com support@viascorp.com
2.
© 2017 Virtual
Integrated Analytics Solutions Inc. Who We Are Engineering Consultancy Training Automation &Customization Software • Solution partner of Dassault Systèmes SIMULIA – Abaqus, Isight, fe-safe, Tosca; CATIA, and DELMIA • Provide Engineering Consultancy, Software Automation and Customization • Multiple Industry Experience – Oil & Gas, Machinery & Equipment, Petrochemical & Process, Nuclear, Aerospace, Medical Devices, Manufacturing and Automotive • Team consists of Ph.D. and Masters in Solid Mechanics, Fluid Mechanics, Materials and Corrosion, Numerical Analysis, Optimization and Reliability, Data Analytics • Additive manufacturing(AM) and Simulation Services 2
3.
© 2017 Virtual
Integrated Analytics Solutions Inc. Data Analytics Examine data to draw conclusions Sophisticated systems and software Informed business decisions Verify scientific theories Machine Learning, Data Mining, Artificial Intelligence 3
4.
© 2017 Virtual
Integrated Analytics Solutions Inc. Machine Learning ▪ Where does machine learning come from? ▪ What is machine learning? ▪ Where can machine learning be applied ▪ Should I care about machine learning at all? 4
5.
© 2017 Virtual
Integrated Analytics Solutions Inc. Where does machine learning come from? Search Artificial Intelligence Planning Knowledge Representation Machine Learning Robotics Clustering Classification Genetic Algorithms Reinforcement Learning Field of Study 5
6.
© 2017 Virtual
Integrated Analytics Solutions Inc. Where does machine learning come from? Machine Learning Probability & Statistics Computational Complexity Theory Information Theory Philosophy Neurobiology Artificial Intelligence Multidisciplinary Field 6
7.
© 2017 Virtual
Integrated Analytics Solutions Inc. Origins: A Brief History McCulloch and Pitts (1943) Model of Artificial Neurons. Donald Hebb (1949) Hebbian Learning Conference at Dartmouth (1956) McCarthy, Minsky, Shannon, Nathaniel, Samuel (IBM), Solomonoff, Newell and Simon. Newell and Simon General Problem Solver 7
8.
© 2017 Virtual
Integrated Analytics Solutions Inc. Later on… The knowledge problem. “the spirit is willing but the flesh is weak” “The vodka is good but the meat is rotten” US government funding was cancelled (1966) Minksy and Papert Book Perceptron (1969) Knowledge based-methods (1969-79) Buchanan with DENDRAL (molecular info. from a mass spectrometer) Expert Systems MYCIN (diagnose blood infections) 8
9.
© 2017 Virtual
Integrated Analytics Solutions Inc. AI and Machine Learning Consolidate (1980 – today) More expert systems. Systems using Prolog. After 1988 companies suffered. The return of Neural Networks Hopfield (1982) AI becomes Science neats beat scruffies Data Mining Bayesian Networks Robotics Computer Vision Machine Learning Artificial General Intelligence Universal algorithm for learning and acting in any environment. 9
10.
© 2017 Virtual
Integrated Analytics Solutions Inc. Machine Learning • Where does machine learning come from? • What is machine learning? • Where can machine learning be applied? • Should I care about machine learning at all? 10
11.
© 2017 Virtual
Integrated Analytics Solutions Inc. What is Machine Learning? Definition Machine learning is the study of how to make computers learn or adapt; the goal is to make computers improve their performance through experience. Experience E Computer Learning Algorithm Class of Tasks T Performance P 11
12.
© 2017 Virtual
Integrated Analytics Solutions Inc. What is Machine Learning? Definition Experience E Computer Learning Algorithm Class of Tasks T Performance P 12
13.
© 2017 Virtual
Integrated Analytics Solutions Inc. What is Machine Learning? Definition It is the kind of activity on which the computer will learn to improve its performance. Examples: Learning to Play chess Recognizing Images of Handwritten Words Diagnosing patients coming into the hospital Class of Tasks: 13
14.
© 2017 Virtual
Integrated Analytics Solutions Inc. What is Machine Learning? Definition Experience E Computer Learning Algorithm Class of Tasks T Performance P 14
15.
© 2017 Virtual
Integrated Analytics Solutions Inc. What is Machine Learning? Definition Experience: What has been recorded in the past Performance: A measure of the quality of the response or action. Example: Handwritten recognition using Neural Networks Experience: a database of handwritten images with their correct classification Performance: Accuracy in classifications Experience and Performance 15
16.
© 2017 Virtual
Integrated Analytics Solutions Inc. What is Machine Learning? Definition 16
17.
© 2017 Virtual
Integrated Analytics Solutions Inc. What is Machine Learning? Definition Experience E Computer Learning Algorithm Class of Tasks T Performance P 17
18.
© 2017 Virtual
Integrated Analytics Solutions Inc. Machine Learning • Where does machine learning come from? • What is machine learning? ▪ Definition ▪ Types of Machine Learning • Where can machine learning be applied? • Should I care about machine learning at all? 18
19.
© 2017 Virtual
Integrated Analytics Solutions Inc. What is Machine Learning? Types of Machine Learning • Supervised Learning • Unsupervised Learning • Reinforcement Learning • Evolutionary Learning 19
20.
© 2017 Virtual
Integrated Analytics Solutions Inc. What is Machine Learning? Types of Machine Learning • Supervised Learning • Each example or object has a class attached to it. • We try to learn a mapping from examples to classes. • Two modes: classification and regression • Machine learning algorithms abound: • Decision Trees • Rule-based systems • Neural networks • Nearest-neighbor • Support-Vector Machines • Bayesian Methods 20
21.
© 2017 Virtual
Integrated Analytics Solutions Inc. Classification or Supervised Learning Supervised Learning: Training set x = {x1, x2, …, xN} Class or target vector y = {y1, y2, …, yk} Find a function f(x) that takes a vector x and outputs a class y. {(x,y)} f(x) {(x,y)} 21
22.
© 2017 Virtual
Integrated Analytics Solutions Inc. What is Machine Learning? Example: Diagnosing a patient coming into the hospital. ▪ Features: ▪ X1: Temperature ▪ X2: Blood pressure ▪ X3: Blood type ▪ X4: Age ▪ X5: Weight ▪ Etc. Given a new example X = < x1, x2, …, xn > F(X) = w1x1 + w2x2 + w3x3 = … + wnxn If F(X) > T predict heart disease otherwise predict no heart disease The Representation of the Target Knowledge Designing a Learning System 22
23.
© 2017 Virtual
Integrated Analytics Solutions Inc. What is Machine Learning? Types of Machine Learning Supervised Learning – Neural Networks Input nodes Internal nodes Output nodes Left Straight Right 23
24.
© 2017 Virtual
Integrated Analytics Solutions Inc. What is Machine Learning? Types of Machine Learning Supervised Learning – Neural Networks Artificial Neural Networks are crude attempts to model the highly massive parallel and distributed processing we believe takes place in the brain. Consider: ▪ the speed at which the brain recognizes images; ▪ the many neurons populating a brain; ▪ the speed at which a single neuron transmits signals. Brain Neuron Model Representation 24
25.
© 2017 Virtual
Integrated Analytics Solutions Inc. What is Machine Learning? Types of Machine Learning Unsupervised Learning Examples or objects have no class attached to them. From “Pattern Classification” by Duda, Hart and Stork, 2nd Ed. Wiley Interscience (2000) 25
26.
© 2017 Virtual
Integrated Analytics Solutions Inc. Clustering or Unsupervised Learning Unsupervised Learning: Training set x = {x1, x2, …, xN} No class or target vector available Find natural groups or clusters in the data {x} 26
27.
© 2017 Virtual
Integrated Analytics Solutions Inc. What is Machine Learning? Types of Machine Learning Reinforcement Learning Supervised Learning: Example Class Reinforcement Learning: Situation Reward Situation Reward … 27
28.
© 2017 Virtual
Integrated Analytics Solutions Inc. What is Machine Learning? Types of Machine Learning Evolutionary Learning Methods inspired by the process of biological evolution. Main ideas Population of solutions Assign a score or fitness value to each solution Retain the best solutions (survival of the fittest) Generate new solutions (offspring) 28
29.
© 2017 Virtual
Integrated Analytics Solutions Inc. Data Mining Selection Target Data Preprocessing Data Preprocessed Data Transformation Transformed Data Patterns Data Mining Interpretation & EvaluationKnowledge Knowledge Discovery and Data Mining 29
30.
© 2017 Virtual
Integrated Analytics Solutions Inc. Machine Learning • Where does machine learning come from? • What is machine learning? • Where can machine learning be applied? • Should I care about machine learning at all? 30
31.
© 2017 Virtual
Integrated Analytics Solutions Inc. Where can machine learning be applied? Automatic car drive (ALVINN 1989) Train computer-controlled vehicle to steer correctly when driving on a variety of road types. computer (learning algorithm) class 1 steer to the left class 2 steer to the right class 3 continue straight 31
32.
© 2017 Virtual
Integrated Analytics Solutions Inc. Where can machine learning be applied? Automatic Car Drive Class of Tasks: Learning to drive on highways from vision stereos. Knowledge: Images and steering commands recorded while observing a human driver. Performance Module: Accuracy in classification 32
33.
© 2017 Virtual
Integrated Analytics Solutions Inc. DARPA Challenge • Competition for driverless vehicles • DARPA – Defense Advanced Research Projects Agency • $2 million dollars – First prize in Oct. 2005 33
34.
© 2017 Virtual
Integrated Analytics Solutions Inc. Where can machine learning be applied? Learning to classify astronomical structures. galaxy stars ▪ Features: ▪ Color ▪ Size ▪ Mass ▪ Temperature ▪ Luminosity unknown 34
35.
© 2017 Virtual
Integrated Analytics Solutions Inc. Where can machine learning be applied? Classifying Astronomical Objects Class of Tasks: Learning to classify new objects. Knowledge: database of images with correct classification. Performance Module: Accuracy in classification 35
36.
© 2017 Virtual
Integrated Analytics Solutions Inc. Where can machine learning be applied? Other Applications ▪ Bio-Technology ▪ Protein Folding Prediction ▪ Micro-array gene expression ▪ Computer Systems Performance Prediction ▪ Banking Applications ▪ Credit Applications ▪ Fraud Detection ▪ Character Recognition (US Postal Service) ▪ Web Applications ▪ Document Classification ▪ Learning User Preferences 36
37.
© 2017 Virtual
Integrated Analytics Solutions Inc. Applications in Science and Industry ▪ Automated seismic data processing ▪ Pattern recognition for creating maps of Mars landforms ▪ Signal identification in particle physics ▪ Predicting stuck pipes during drilling ▪ Data analytics for computer systems management 37
38.
© 2017 Virtual
Integrated Analytics Solutions Inc. Seismic data processing • Seismic processing can take months and require world’s most powerful computers • Need for automated tools for identification and delineation of geological elements from 3D seismic data. • Algorithms include the use of higher order statistics, feature extraction methods, pattern recognition, clustering methods and unsupervised classification. • Automating the process of identifying geological bodies • Significant efficiency improvements • Improves accuracy of predictions • Results in millions of dollars of value to our clients 38
39.
© 2017 Virtual
Integrated Analytics Solutions Inc. Automatic Classification of Mars Landforms • Identifying landforms on Mars is a tedious manual process taking an enormous amount of time • Machine learning techniques are used to train a model with a small set of labeled segments • Predictive models have shown accuracies of approximately 90% • The automated solution produces a complete catalog of landforms on Mars • Similar approach can be applied to processing seismic data Plain Crater Floor Convex Crater Walls Concave Crater Walls Convex Ridges Concave Ridges 39
40.
© 2017 Virtual
Integrated Analytics Solutions Inc. Identification of Signals in Particle Physics • Searching for particle signals in particle colliders data is a challenging problem due to large backgrounds • Data mining tools are used to the search for single top quark production by using predictive models that identify top quark patterns • This allows to obtain evidence for the existence of certain particles that otherwise go unnoticed during costly experiments • Similar approach can be used for identification of certain features in geophysical data 40
41.
© 2017 Virtual
Integrated Analytics Solutions Inc. Real-time Stuck Drillpipe Prediction • Stuck drill pipes is a major cost driver in the drilling industry • Using machine learning with real-time monitoring can predict stuck events before they actually occur • Predictive models allow drillers to react before any critical event • Pilot studies show 95% effectiveness in predicting stuck pipes 15 mins ahead of time. Safe time window for prediction Stuck Pipe Event 41
42.
© 2017 Virtual
Integrated Analytics Solutions Inc. Computer Network Performance Prediction • Critical operations in industry cannot afford losing a critical computer node • Data mining finds activity patterns that anticipate computer node failure • Anticipating node failure activates proactively a procedure to avoid service interruption during critical operations • Similar approach can be applied to detect anomalies in production operations 42
43.
© 2017 Virtual
Integrated Analytics Solutions Inc. Machine Learning • Where does machine learning come from? • What is machine learning? • Where can machine learning be applied? • Should I care about machine learning at all? 43
44.
© 2017 Virtual
Integrated Analytics Solutions Inc. Should I care about Machine Learning at all? • Yes, you should! • Machine learning is becoming increasingly popular and has become a cornerstone in many industrial applications. • Machine learning provides algorithms for data mining, where the goal is to extract useful pieces of information (i.e., patterns) from large databases. • The computer industry is heading towards systems that will be able to adapt and heal themselves automatically. • The Oil and Gas industry is now focusing on data analytics as a game changer through the automation of pattern recognition engines. • NASA and Military Agencies are interested in robots able to adapt in any environment autonomously. • The Medical industry is now using machine learning to diagnose diseases. 44
45.
© 2017 Virtual
Integrated Analytics Solutions Inc. Machine Learning Course http://www.viascorp.com/course-schedule/ 45
46.
© 2017 Virtual
Integrated Analytics Solutions Inc. Deep Learning Course http://www.viascorp.com/course-schedule/ 46
47.
© 2017 Virtual
Integrated Analytics Solutions Inc. Python Course http://www.viascorp.com/course-schedule/ 47
48.
© 2017 Virtual
Integrated Analytics Solutions Inc. Thank you 48