What is Data Analysis and Machine Learning?

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

© 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

Data Analytics
Examine data
to draw conclusions
Sophisticated systems
and software
Informed business
decisions
Verify scientific
theories
Machine Learning, Data
Mining, Artificial Intelligence
3

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

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

Where does machine learning
come from?
Machine
Learning
Probability &
Statistics
Computational
Complexity
Theory Information
Theory
Philosophy
Neurobiology
Artificial
Intelligence
Multidisciplinary Field
6

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

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

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

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

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

Definition
Experience E
Computer
Learning
Algorithm
12

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

Definition
Experience E
Computer
Learning
Algorithm
14

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

Definition
16

Definition
Experience E
Computer
Learning
Algorithm
17

Machine Learning
▪ Definition
▪ Types of Machine Learning
18

Types of Machine Learning
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Evolutionary Learning
19

• 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

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

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

Supervised Learning – Neural Networks
Input nodes
Internal nodes
Output nodes
Left Straight Right
23

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

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

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

Reinforcement Learning
Supervised Learning:
Example Class
Reinforcement Learning:
Situation Reward Situation Reward
…
27

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

Data Mining
Selection
Target Data
Preprocessing
Data Preprocessed
Data
Transformation
Transformed
Data
Patterns
Data
Mining
Interpretation &
EvaluationKnowledge
Knowledge Discovery and Data Mining
29

Machine Learning
30

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

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

DARPA Challenge
• Competition for driverless vehicles
• DARPA – Defense Advanced Research Projects Agency
• $2 million dollars – First prize in Oct. 2005
33

be applied?
Learning to classify astronomical structures.
galaxy
stars
▪ Features:
▪ Color
▪ Size
▪ Mass
▪ Temperature
▪ Luminosity
unknown
34

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

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

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

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

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

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

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

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

Machine Learning
43

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

Machine Learning Course
http://www.viascorp.com/course-schedule/
45

Deep Learning Course
46

Python Course
47

Thank you
48

What is Data Analysis and Machine Learning?

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to What is Data Analysis and Machine Learning?

Similar to What is Data Analysis and Machine Learning? (20)

More from Arindam Chakraborty, Ph.D., P.E. (CA, TX)

More from Arindam Chakraborty, Ph.D., P.E. (CA, TX) (20)

Recently uploaded

Recently uploaded (20)

What is Data Analysis and Machine Learning?