ORIGIN, DEVELOPMENT AND
USES OF MACHINE LEARNING
K V R PRANAV1, K J
SARMA2
Student College
of Engineering,
Freelance Research
Mangalpalli,
Retired Professor
kvrpranav@gmail.com, kjsarmalsm@ieee.org
ABSTRACT:
It is necessary to trace the studies
made on any topic from the origin and the genesis so that, we can model the practical
situations. The present overview of ML is a small attempt to trace the developments
and applications, so that we can reduce the gap between theory and practice .
KEY
WORDS: machine learning,
application in health care, time line of developments, supervised,
unsupervised, reinforcement and semi supervised
learning in machines
INTRODUTION: Machine learning (ML)
is a field of inquiry devoted to understanding and building methods that
'learn', methods that leverage data to improve performance on some set of
tasks. It is seen as a part
of artificial intelligence. Machine
learning algorithms build a model based on sample data, called as training data,
which allows us to make predictions or
decisions. Machine learning
algorithms are used in a wide variety of applications, related to
medicine, email filtering, speech recognition, agriculture,
and computer vision, when it is difficult
to develop conventional algorithms to perform the tasks. A subset of machine learning is
closely related to computational statistics,
which focuses on making predictions using computers. The term machine learning was coined in 1959 by Arthur Samuel,
an IBM employee
and pioneer in the field of computer gaming and artificial intelligence.
By the early 1960s an experimental
"learning machine" with punched tape memory,
called Cybertron, had been developed by Raytheon Company to
analyze sonar signals, electrocardiograms, and speech patterns using
rudimentary reinforcement learning. It was repetitively
"trained" by a human operator to recognize patterns to re-evaluate
incorrect decisions. A
representative book on research into machine learning during the 1960s by Nilsson's
on Learning Machines, deals with machine learning for pattern classification. Interest related to pattern recognition
continued into the 1970s, as described by Duda and Hart in 1973. In 1981 a report was given on using
teaching strategies so that a neural network learns
to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from
a computer terminal.
Tom M. Mitchell provided
a formal definition of the algorithms studied in the machine learning field:
"A computer program is said to learn from experience E with respect to some class of
tasks T and
performance measure P if
its performance at tasks in T,
as measured by P, improves
with experience E." This follows Alan Turing's
proposal in his paper "Computing Machinery and Intelligence",
in which the question "Can machines think?" is replaced with the
question "Can machines do what we (as thinking entities) can do?”
Modern-day machine
learning has two objectives, one is to classify data based on models which have
been developed, and the other purpose is to make predictions for future
outcomes based on these models. A hypothetical algorithm to classifying data
may use computer vision of moles coupled with supervised learning in order to
train it to classify the cancerous moles. A machine learning algorithm for
stock trading may inform the trader of future potential predictions
Machine learning
programs can perform tasks without being explicitly programmed to do so. It
involves computers learning from data provided, so that they carry out certain
tasks. For simple tasks assigned to computers, it is possible to design algorithms.
Thus telling the machine how to execute all steps required to solve the problem
at hand; on the computer's. For more
advanced tasks, it can be challenging for a human to manually create the needed
algorithms. In practice, it can turn out to be more effective to help the
machine develop its own algorithm, rather than having human programmers specify
every step.
Machine Learning is a branch of the broader field of artificial
intelligence that makes use of statistical models to develop predictions. In basic technical terms, machine learning
uses algorithms that take empirical or historical data in, analyze it, and
generate outputs based on that analysis.
Machine Learning is the field of study that gives computers the
capability to learn without being explicitly programmed. ML is one of the most
exciting technologies that one would have ever come across. Of course it is evident from the name, It gives the computer that makes it more
similar to humans we call the
ability to learn. Machine learning is actively used today, perhaps in
many more situations than one would expect.
Machine learning (ML) is a field of inquiry devoted to understanding and building
methods that 'learn', that is, methods that leverage data to improve
performance on some set of tasks. Machine learning (ML) is a subset of artificial intelligence (AI) that allows
software applications to become more accurate at predicting outcomes.
In fact Artificial intelligence can
enable systems to identify patterns in data, make decisions. and predict future
outcomes. Whereas Machine learning can help companies determine the products
you're most likely to buy and even the online content you're most likely to
consume and enjoy. Machine learning
makes it easier to analyze and interpret massive amounts of data, which would
otherwise take decades. It can automate
many tasks, especially the ones that only humans can perform with their innate
intelligence. It also helps in automating and quickly creates models for data
analysis. Various industries depend on vast quantities of data to optimize
their operations and make intelligent decisions. It helps in creating models
that can process and analyze large amounts of complex data to deliver accurate
results.
These ML models are
precise and scalable and function with less turnaround time. By building such
precise Machine Learning models, businesses can leverage profitable
opportunities and avoid unknown risks. Image recognition, text generation, and
many other use-cases are finding applications in the real world, this is
increasing the scope for machine learning experts.
Machine learning is
incredibly complex and how it works varies depending on the task and the
algorithms used to accomplish it. However, at its core, a machine learning
model is a computer looking at data and identifying patterns, and then using
those insights to complete its assigned task more effectively. Any task that
relies upon a set of data points or rules can be automated using machine
learning, including responding to customer service calls and reviewing CVs.
This pervasive and
powerful ML of artificial intelligence is changing every industry. When
companies today deploy artificial intelligence programs, they are most likely
using machine learning — so much so that the terms are often used
interchangeably. Machine learning is a subfield of artificial intelligence that
gives computers the ability to learn without explicitly being programmed.
By applying machine learning
techniques, companies are gaining significant competitive and
financial advantages in delivering better customer experiences and reacting
more swiftly to market shifts. Machine learning is widely used today in web search, spam filters, recommender
systems, ad placement, credit scoring, fraud detection, stock trading, drug
design, and many other applications.
Advantages of Machine
Learning: The benefits businesses
gain from machine learning
are
*Quickly discover
specific trends, patterns and implicit relationships in vast, complex datasets
*Has
the ability to learn and make predictions without human intervention
*Continuous
improvement in accuracy, efficiency, and speed
*Good
at handling multidimensional problems and multivariate data
*Help
businesses make smarter and faster decisions in real-time
*Eliminate
bias from human decision making
*Automate
and streamline predictable and repetitive business processes
*Better
use of data – both structured and unstructured.
HOW
DOES IT WORK : Machine Learning is, undoubtedly, one of
the most exciting subsets of Artificial Intelligence. It completes the task of
learning from data with specific inputs to the machine. It’s important to
understand what makes Machine Learning work and, thus, how it can be used in
the future.
The Machine Learning
process starts with inputting training data into the selected algorithm.
Training data being known or unknown is used to develop the final Machine
Learning algorithm. The type of training data input does impact the algorithm,
and that concept will be covered further momentarily.
New input data is fed
into the machine learning algorithm to test whether the algorithm works
correctly. The prediction and results are then checked against each other.
If the prediction and
results don’t match, the algorithm is re-trained multiple times until the data
scientist gets the desired outcome. This enables the machine learning algorithm
to continually learn on its own and produce the optimal answer, gradually
increasing in accuracy over time.
TIME LINE OF STUDIES
CARRIED OUT ON M.L.
We trace the origin and
short history of machine learning and its most important milestones.
18th century —statistical methods: Several vital
concepts in machine learning derive from probability theory and statistics, and
they root back to the 18th century. In 1763, English statistician Thomas Bayes
set out a mathematical theorem for probability, which came to be known as Bayes
Theorem that a central concept of machine learning.
1950 — The Turing Test: mathematician Alan Turing’s
papers in the 1940s were full of ideas on machine intelligence. “Can machines
think?”, paving way for automata theory. In 1950, he suggested a test for
machine intelligence, later known as the Turing Test, in which a machine is said
to be “intelligent” if it could convince a human.
1952 — Game of Checkers: In 1952, researcher Arthur
Samuel created an early learning machine, capable of learning to play checkers.
It is an annotated guides to learn to
distinguish right moves from bad.
1956 — The Dartmouth Workshop: The term
‘artificial intelligence’ was born during the Dartmouth Workshop in 1956, The
workshop scientists, including computer scientist John McCarthy, Marvin Minsky,
Nathaniel Rochester, and Claude Shannon.
1957 — The Perceptron: Noted American psychologist
Frank Rosenblatt’s Perceptron was an early attempt to create a neural network
with the use of a rotary resistor (potentiometer) driven by an electric motor.
The machine could create an output.
1967 — Nearest neighbor algorithm: The Nearest
Neighbor (NN) rule in pattern recognition, which enabled article written by T. Cover and P. Hart in
1967. The algorithm gave an idea of
solution to traveling sales problem.
1973 — The Light hill report and the AI winter:
James Light hill in 1973, presented a
very pessimistic forecast in the development of core aspects in AI research”.
1979 — Stanford Cart: The students at Stanford
University invented a robot called the Cart, radio-linked to a large mainframe
computer, which can navigate obstacles in a room on its own. The invention was
state of the art at the time.
1981 — Explanation Based Learning (EBL): Gerald
Dejong introduced the concept of Explanation Based Learning (EBL), which
analyses data and creates a general rule it can follow by discarding
unimportant data.
1985 — Net Talk: Francis Crick Professor Terry
Sejnowski invented NetTalk, NETtalk, a program that learns to pronounce written
English text by being shown text as input and matching phonetic transcriptions
for comparison. This has shed light on human learning.
1986 — Parallel Distributed Processing and neural network models: David
Rumelhart and James McClelland published Parallel Distributed Processing, which
advanced the use of neural network models for machine learning.
1992 — playing backgammon: Researcher Gerald
Tesauro created a program based on an artificial neural network, which was capable
of playing backgammon with abilities that matched top human players.
1997 — deep Blue: IBM’s Deep Blue became the first
computer chess-playing system to beat a reigning world chess champion. Deep
Blue used the computing power in the 1990s to perform large-scale searches of
potential moves and select the best move.
2006 — Deep Learning: Geoffrey Hinton created the
term “deep learning” to explain new algorithms that help computers distinguish
objects and text in images and videos.
2010 — Kinect: Microsoft developed the
motion-sensing input device named Kinect that can track 20 human
characteristics at a rate of 30 times per second. It allowed people to interact
with the computer through movements and gestures.
2011 — Watson and Google Brain: IBM’s Watson won
a game of the US quiz show Jeopardy against two of its champions. In the same
year, Google Brain was developed its deep neural network which could discover
and categorize objects in the way a cat does.
2012 — ImageNet Classification and computer vision: The
year saw the publication of an influential research paper by Alex Krizhevsky,
Geoffrey Hinton, and Ilya Sutskever, describing a model that can dramatically
reduce the error rate in image recognition systems. Meanwhile, Google’s X Lab
developed a machine learning algorithm capable of autonomously browsing YouTube
videos to identify the videos that contain cats.
2014 — Deep Face: Facebook developed a software
algorithm Deep Face, which can recognize and verify individuals on photos with
an accuracy of a human.
2015 — Amazon Machine Learning: AWS’s Andy Jassy
launched their Machine Learning managed services that analyze users’ historical
data to look for patterns and deploy predictive models. In the same year,
Microsoft created the Distributed Machine Learning Toolkit, which enables the
efficient distribution of machine learning problems across multiple computers.
2016 — AlphaGo: AlphaGo, created by researchers at
Google Deep Mind to play the ancient Chinese game of Go, won four out of five
matches against Lee Sedol, who has been the world’s top Go player for over a
decade.
2017 — Libratus and Deepstack: Researchers at
Carnegie Mellon University created a system named Libratus, and it defeated
four top players at No Limit Texas Hold .
Machine
learning time line from 18 th century
2017:
Image Net Challenge – Milestone in the History of Machine Learning. The Image Net Challenge is a competition in computer
vision that has been running since 2010. This challenge focuses on the
abilities of programs to process
patterns in images and recognize objects with varying degrees. In 2017, a milestone was reached. 29 out of 38 teams achieved 95% accuracy
with their computer vision models. The improvement in image recognition is
immense.
ML into the future
Present: State-of-the-art Machine Learning-- Machine learning is
used in many different fields, from fashion to agriculture. Machine Learning
algorithms are able to learn patterns and relationships between data, find
predictive insights for complex problems and extract information that is
otherwise too difficult to find. Today’s Machine Learning algorithms are able
to handle large amounts of data with accuracy in a relatively short amount of
time.
Prerequisites
for Machine Learning (ML) are, For
those interested in learning beyond what is Machine Learning, a few
requirements should be met to be successful in pursual of this field. These
requirements include:
*Basic
knowledge of programming languages such as Python, R, Java, JavaScript, etc
*Intermediate
knowledge of statistics and probability
*Basic
knowledge of linear algebra. In the linear regression model, a line is drawn
through all the data *points, and that line is used to compute new values.
*Understanding
of calculus
*Knowledge
of how to clean and structure of raw data to the desired format to reduce the
time taken for decision-making.
TYPES AND ALGORITHMS IN
MACHINE LEARNING:
Depending on the
situation, machine learning algorithms function using more or less human
intervention/reinforcement. The four major machine learning models are supervised learning, unsupervised learning,
semi-supervised learning and reinforcement learning.
More
specifically, Machine Learning is complex. Approximately 70 percent of machine
learning is supervised learning, while unsupervised learning accounts for
anywhere from 10 to 20 percent. The remainder is taken up by reinforcement
learning.
1. Supervised Learning:
In
supervised learning, we use known or labeled data for the training data. Since
the data is known, the learning is
therefore, supervised, i.e., directed into successful execution. The input data
goes through the Machine Learning algorithm and is used to train the model.
Once the model is trained based on the known data, you can use unknown data
into the model and get a new response.
In this case, the model
tries to figure out whether the data is an apple or another fruit. Once the
model has been trained well, it will identify that the data is an apple and
give the desired response. The list of top algorithms currently being used
for supervised learning are: Polynomial
regression; Random forest; Linear regression; Logistic regression; Decision trees; K-nearest neighbors; Naive Bayes; Now
let’s learn about unsupervised learning.
2.
Unsupervised Learning; In
unsupervised learning, the training data is unknown and unlabeled - meaning
that no one has looked at the data before. Without the aspect of known data,
the input cannot be guided to the algorithm, which is where the unsupervised
term originates from. This data is fed to the Machine Learning algorithm and is
used to train the model. The trained model tries to search for a pattern and
give the desired response. Here the algorithm is trying to break code like the
Enigma machine but without the human mind directly.. There are many different ways that machine
learning algorithms do this, including:
*Clustering, in which
the computer finds similar data points within a data set and groups them
accordingly (creating “clusters”).
*Density estimation in
which the computer discovers insights by looking at how a data set is
distributed.
*Anomaly detection, in
which the computer identifies data points within a data set that are
significantly different from the rest of the data.
*Principal component
analysis (PCA), in which the computer analyses a data set and summarises it so
that it can be used to make accurate predictions
In this case, the
unknown data consists of apples and pears which look similar to each other. The
trained model tries to put them all together so that you get the same things in
similar groups. Some of the top 7 algorithms currently being used for
unsupervised learning are: Partial least squares; Fuzzy means;
Singular value decomposition; K-means clustering; Apriori; Hierarchical clustering; Principal component analysis.
ML solves problems that cannot be solved by numerical means alone. Among the different
types of ML tasks, a crucial distinction is drawn between supervised and
unsupervised learning. Supervised
machine learning is when the program is “trained” on a predefined set
of “training examples,” which then facilitate its ability to reach an accurate
conclusion when given new data. Unsupervised
machine learning is when the program is given a bunch of data and must
find patterns and relationships therein.
With semi-supervised learning, the
computer is provided with a set of partially labeled data and performs its task
using the labeled data to understand the parameters for interpreting the
unlabeled data.
3.
Reinforcement Learning: With reinforcement
learning, the computer observes its environment and uses that data
to identify the ideal behavior that will
minimizes risk and/or maximizes reward. This is an iterative approach that
requires some kind of reinforcement signal to help the computer better identify
its best action.
On the whole Machine learning (ML) allows
software applications to become more accurate at predicting outcomes, without
being explicitly programmed. Machine learning algorithms use
historical data as input to predict new output values. This machine learning is closely related
to computational statistics,
which focuses on making predictions using computers.
Like traditional types
of data analysis, here, the algorithm discovers data through a process of trial
and error and then decides what action results in higher rewards. Three major
components make up reinforcement learning: the agent, the environment, and the
actions. The agent is the learner or decision-maker, the environment includes
everything that the agent interacts with, and the actions are what the agent
does.
Reinforcement learning happens
when the agent chooses actions that maximize the expected reward over a given
time. This is easiest to achieve when the agent is working within a policy
framework. Machine Learning is such a
vital concept of modern times.
To better answer the question:
what is machine learning” and understand the uses of Machine Learning we consider some of the applications of Machine
Learning; the self-driving Google car,
cyber fraud detection, and online recommendation engines from Facebook,
Netflix, and Amazon. Machines make all these things possible by filtering
useful pieces of information based on patterns to get accurate results.
The rapid evolution in
Machine Learning (ML) has caused a subsequent rise in the use cases, demands,
and the sheer importance of ML in modern life. Big Data has also become a
well-used buzzword in the last few years. This may be due to the
increased sophistication of Machine Learning, which enables the analysis of
large chunks of Big Data. Machine Learning has also changed the way data
extraction and interpretations are can be made by automating generic
methods/algorithms, thereby replacing traditional statistical techniques.
HOW TO DECIDE WHICH MACHINE
LEARNING ALGORITHM SHOULD BE USED?
There
are dozens of different algorithms to choose from, but there’s no best choice
or one that suits every situation. In many cases, you must resort to trial and
error. But there are some questions we can ask that can help narrow down the
choices.
*What’s
the size of the data you will be working with?
*What’s
the type of data you will be working with?
*What
kinds of insights are you looking for from the data?
*How
will those insights be used?
*What
is the Best Programming Language for Machine Learning?
If we
look at the choices based on sheer popularity, then Python gets the nod. Python is ideal for data analysis and data
mining and supports many algorithms (for
classification, clustering, regression, and dimensionality reduction), and
machine learning models.
Enterprise
Machine Learning and MLOps : Enterprise
machine learning gives businesses important insights into customer loyalty and
behavior, as well as the competitive business environment. Machine learning
also can be used to forecast sales or real-time demand.
Machine learning
operations (MLOps) is the discipline of Artificial Intelligence model delivery.
It helps organizations scale production capacity to produce faster results,
thereby generating vital business value.
Some
Machine Learning Algorithms and Processes:
In order to familiarize with standard Machine Learning algorithms and
processes, we have to learn neural networks, decision trees, random
forests, associations, and sequence discovery, gradient boosting and bagging,
support vector machines, self-organizing maps, k-means clustering, Bayesian
networks, Gaussian mixture models, and more. There are other machine learning tools and
processes that leverage various algorithms to get the most value out of big
data. These include:
*Comprehensive
data quality and management
*GUIs
for building models and process flows
*Interactive
data exploration and visualization of model results
*Comparisons
of different Machine Learning models to quickly identify the best one
*Automated
ensemble model evaluation to determine the best performers
*Easy
model deployment so you can get repeatable, reliable results quickly
*An
integrated end-to-end platform for the automation of the data-to-decision
process
MACHINE LEARNING STEPS : The task of imparting
intelligence to machines seems daunting and impossible. But it is actually
really easy. It can be broken down into 7 major steps:
1. Collecting Data: As you know, machines initially
learn from the data that you give them. It is
of the utmost importance to collect reliable data so that your machine learning
model can find the correct patterns. The quality of the data that you feed to
the machine will determine how accurate your model is. If you have incorrect or
outdated data, you will have wrong outcomes or predictions which are not
relevant.
Make
sure you use data from a reliable source, as it will directly affect the
outcome of your model. Good data is relevant, contains very few missing and
repeated values, and has a good representation of the various
subcategories/classes present.
2. Preparing the Data: After
you have your data, you have to prepare it. You can do this by :
·
Putting
together all the data you have and randomizing it. This helps make sure that
data is evenly distributed, and the ordering does not affect the learning
process.
·
Cleaning
the data to remove unwanted data, missing values, rows, and columns, duplicate
values, data type conversion, etc. You might even have to restructure the
dataset and change the rows and columns or index of rows and columns.
·
Visualize the data to understand how it is
structured and understand the relationship between various variables and
classes present.
·
Splitting
the cleaned data into two sets - a training set and a testing set. The training
set is the set your model learns from. A testing set is used to check the
accuracy of your model after training.
3. Choosing a Model: A machine learning model
determines the output you get after running a machine learning algorithm on the
collected data. It is important to choose a model which is relevant to the task
at hand. Over the years, scientists and engineers developed various models
suited for different tasks like speech recognition, image recognition,
prediction, etc. Apart from this, you also have to see if your model is suited
for numerical or categorical data and choose accordingly.
4. Training the Model: Training
is the most important step in machine learning. In training, you pass the
prepared data to your machine learning model to find patterns and make
predictions. It results in the model learning from the data so that it can
accomplish the task set. Over time, with training, the model gets better at
predicting.
5. Evaluating the Model: After
training your model, you have to check to see how it’s performing. This is done
by testing the performance of the model on previously unseen data. The unseen
data used is the testing set that you split our data into earlier. If testing
was done on the same data which is used for training, you will not get an
accurate measure, as the model is already used to the data, and finds the same
patterns in it, as it previously did. This will give you disproportionately
high accuracy. When used on
testing data, you get an accurate measure of how your model will perform and
its speed.
6. Parameter Tuning: Once you have created and
evaluated your model, see if its accuracy can be improved in any way. This is
done by tuning the parameters present in your model. Parameters are the
variables in the model that the programmer generally decides. At a particular
value of your parameter, the accuracy will be the maximum. Parameter tuning
refers to finding these values.
7. Making Predictions In the end, you can use your model
on unseen data to make predictions accurately.. How to Implement Machine
Learning Steps in Python?
In an
example of data collected is from an insurance company, which tells you the
variables that come into play when an insurance amount is set. Using this, you
will have to predict the insurance amount for a person. This data was collected
from Kaggle.com, which has many reliable datasets.
·
You
need to start by importing any necessary modules. Following this, you will import the
data.
·
Now,
clean your data by removing duplicate values, and transforming columns into
Numerical values to make them easier to work
with.
·
Now,
split your dataset into training and testing sets.
·
As
you need to predict a numeral value based on some parameters, you will have to
use Linear Regression. The model needs to learn on your training set. This is
done by using the '.fit' command.
·
Now,
predict your testing dataset and find how accurate your predictions are.
·
Now,
get your parameters.
·
The
above picture shows the hyper-parameters which affect the various variables in
your dataset.
·
Stay
ahead of the tech-game with our PG Program in AI and Machine Learning in
partnership with Purdue and in collaboration with IBM.
To
learn more about machine learning and how to make machine learning models,
check out Simplilearn’s AI and Machine Learning Course If you have any
questions or doubts, mention them in this article's comments section, and we'll
have our experts answer them for you at the earliest.
USES OF BAYES THEOREM
IN MACHINE LEARNING: Developing classifier models may be the
most common application on Bayes Theorem
in machine learning. Of course there
are many other applications. Two important examples are optimization and causal
models.
I.
Bayesian Optimization
Global optimization is
a challenging problem of finding an input that results in the minimum or maximum
cost of a given objective function. Typically,
the form of the objective function is complex and intractable to analyze and is
often non-convex, nonlinear, high dimension, noisy, and computationally
expensive to evaluate.
Bayesian Optimization
provides a principled technique based on Bayes Theorem to direct a search of a
global optimization problem that is efficient and effective. It works by
building a probabilistic model of the objective function, called the surrogate function
that is then searched efficiently with an acquisition function before candidate
samples are chosen for evaluation on the real objective function.
Bayesian Optimization
is often used in applied machine learning to tune the hyperparameters of a
given well-performing model on a validation dataset.
II.
Bayesian Belief Networks:
Probabilistic
models can define relationships between variables and be used to calculate
probabilities.
Fully conditional
models may require an enormous amount of data to cover all possible cases, and
probabilities may be intractable to calculate in practice. Simplifying
assumptions such as the conditional independence of all random variables can be
effective, such as in the case of Naive Bayes, although it is a drastically
simplifying step.
An alternative is to
develop a model that preserves known conditional dependence between random
variables and conditional independence in all other cases. Bayesian networks
are a probabilistic graphical model that explicitly captures the known
conditional dependence with directed edges in a graph model. All missing
connections define the conditional independencies in the model. As such Bayesian Networks provide a useful
tool to visualize the probabilistic model for a domain, review all of the
relationships between the random variables, and reason about causal
probabilities for scenarios given available evidence.
The networks are not
exactly Bayesian by definition, although given that both the probability
distributions for the random variables (nodes) and the relationships between
the random variables (edges) are specified subjectively, the model can be
thought to capture the “belief” about a complex domain.
1II.
Bayesian Machine Learning Applications Examples,
Bayesian machine
learning is one of the most powerful tools in data
analytics. Bayes’ theorem, which was first introduced by Reverend Thomas Bayes
in 1764, provides a way to infer probabilities from observations. Bayesian
machine learning has become increasingly popular because it can be used for
real-world applications such as credit card fraud detection and spam filtering.
In this blog post, we will discuss Bayesian
machine learning real-world examples to help you understand how
Bayes’ theorem works.
Bayesian machine
learning utilizes Bayes’ theorem to predict occurrences. Bayesian inference is
grounded in Bayes’ theorem, which allows for accurate prediction when applied
to real-world applications. Here are some great examples of real-world
applications of Bayesian inference:
1.
Credit card fraud detection:
Bayesian inference can identify patterns or clues for credit card fraud by analyzing
the data and inferring probabilities with Bayes’ theorem. Credit card fraud detection may have false
positives due to incomplete information. After an unusual activity is reported
to enterprise risk management, Bayesian neural network techniques are used on
the customer profile dataset that includes each customer’s financial
transactions over time. With these analyses we can confirm whether there are
any indications of fraudulent activities.
2.
Spam filtering:
Bayesian inference allows for the identification of spam messages by using
Bayes’ theorem to construct a model that can tell if an email is likely to be
spam or not. The Bayesian model trained using the Bayesian algorithm will take
each word in the message into account and give it different weights based on
how often they appear in both spam and non-spam messages. Bayesian neural networks are also
used to classify spam emails by looking at the probability of an email being
spam.
3.
Medical diagnosis: Bayes’ theorem
is applied in medical diagnoses to use data from previous cases and determine
the probability of a patient having a certain disease. Bayesian inference
allows for better prediction than traditional statistic methods because it can
take into account all the factors that may affect an outcome and provide
probabilities instead of just binary results. Bayes’ theorem is used to compute
posterior probabilities, which are combined with clinical knowledge about
diseases and symptoms to estimate the likelihood of a condition. Bayesian
inference is used in the diagnosis of Alzheimer’s disease by analyzing past patient data and
finding a pattern that can indicate whether a person has this condition. Bayes’
theorem is especially useful for rare
diseases that may occur infrequently and require a large amount of
data to make accurate predictions.
4.
Patterns in customer
dataset/marketing campaign performance: Bayesian
nonparametric clustering technique is used to find hidden patterns in
data. Bayesian nonparametric
clustering technique (BNC) is a powerful method that can be applied
to various datasets such as customer datasets or marketing campaign
performance. It helps find hidden patterns in data because Bayesian machine
learning does not require any assumptions about the distribution of input
variables. BNC enables you to find clusters that are statistically significant
and can be generalized across other datasets as well.
5.
Help robots make decisions: Bayesian
inference is used in robotics to help robots make decisions. Bayes’ theorem can
be applied by using real-time sensor information from the robot’s environment
and inferring about its next move or action based on previous experiences.
Robots will use Bayes’ theorem for extracting relevant features such as speed,
the direction of movement, obstacles, and other objects in the environment.
Bayesian reinforcement learning can be applied to robot learning. Bayesian reinforcement learning (BRL) uses
Bayes’ theorem to compute the probability of taking a certain action based on
previously learned experiences/knowledge and observations received from sensory
information. BRL has been shown to outperform other machine learning algorithms
such as deep Q-learning, Monte Carlo Tree
Search, and Temporal Difference Learning.
6.
Reconstructing clean images from
noisy images:
Bayes’ theorem is used in Bayesian inverse problems such as Bayesian tomography. Bayesian
inference can be applied to the problem of reconstructing images from noisy
versions of those images using Bayes’ theorem and Markov Chain Monte Carlo
(MCMC) algorithms.
7.
Weather prediction:
Bayesian inference can be used in Bayesian machine learning to predict the
weather with more accuracy. Bayes’ theorem can be applied for predicting
real-time weather patterns and probabilities of rain based on past data such as
temperature, humidity, etc. Bayesian models compare favorably against classical
approaches because they take into account the historical behavior of the system
being modeled and provide a probability distribution over the possible outcomes
of the forecast.
8.
Speech emotion recognition:
Nonparametric hierarchical neural network (NHNN), a lightweight hierarchical
neural network model based on Bayesian nonparametric clustering (BNC), can be
used to recognize emotions in speech with better accuracy. NHNN models generally
outperform the models with similar levels of complexity and state-of-the-art
models in within-corpus and cross-corpus tests. Through clustering analysis, is
is shown that the NHNN models are able to learn group-specific features and
bridge the performance gap between groups.
9.
Estimating gas emissions: The recent
findings suggest that a large fraction of anthropogenic methane emissions is
represented by abnormal operating conditions of oil and gas equipments. As
such, effective mitigation requires rapid identification as well as repairs for
faulty sources controlled via advanced sensing technology or automatic fault
detection algorithms based on recursive Bayes’ techniques.
10.
Federated analytics (Faulty device
detection, malfunctions) :
Bayesian approach can be applied to federated analytics, a new approach to data
analytics involving an integrated pipeline of machine learning techniques. The
Bayesian hierarchical model allows the user to interrogate the aggregated model
and automatically detect anomalies that could indicate faulty devices,
malfunctions, or other such problems with remote assets/sensor networks.
Federated learning is the methodology that provides a means of decentralized
computations for machine learning without a need for moving local data of
users. In each round of the federated learning, the participating devices train
a model on their respective local data and send only an encrypted update to the
aggregator. The aggregator combines updates from all participants to improve a
shared model followed by its distribution to all participants.
11.
Forensic analysis: Bayesian
inference can be used in Bayesian machine learning to infer the identity of an
individual based on DNA evidence. Bayes’ theorem is applied for forensic
analysis, which involves reasoning about conditional probabilities and making
statistical inferences from observed data (genetic marker alleles) with respect
to one or more populations of possible genotypes under study.
12.
Optical character recognition (OCR): Bayesian
inference can be used in Bayesian machine learning to improve optical character
recognition (OCR) performance. Bayes’ theorem is applied for OCR, which
involves the transformation of images captured on paper-based media into text
strings that are computer-readable. Bayesian approaches have been shown to
provide more accurate results compared with conventional machine learning
algorithms.
Bayesian machine
learning is a subset of Bayesian statistics that makes use of Bayes’ theorem to
draw inferences from data. Bayesian
inference can be used in Bayesian machine learning to predict the weather with
more accuracy, recognize emotions in speech, estimate gas emissions, and
much more! If you’re interested in learning more about how Bayes’ theorem could
help, let us know.
MAIN
USES OF MACHINE LEARNING: Typical
results from machine
learning applications usually include web search
results, real-time ads on web pages and mobile devices, email spam filtering,
network intrusion detection, and pattern and image recognition. All these are
the by-products of using machine learning to analyze massive volumes of data.
Traditionally, data
analysis was trial and error-based, an approach that became increasingly
impractical thanks to the rise of large, heterogeneous data sets. Machine
learning provides smart alternatives for large-scale data analysis. Machine
learning can produce accurate results and analysis by developing fast and
efficient algorithms and data-driven models for real-time data processing.
Pro Tip: For more on
Big Data and how it’s revolutionizing industries globally, check out our “What is Big Data?” Article.
According to Market watch, the
global machine learning market is expected to grow at a healthy rate of over
45.9 percent during the period of 2017-2025. If this trend holds, then we will
see a greater use of machine learning across a wide spectrum of industries
worldwide. Machine learning is here to stay.
Some of the 10 Popular Machine
Learning Algorithms are based on Linear regression; Logistic regression; Decision tree; SVM algorithm; Naive Bayes algorithm; KNN algorithm; K-means;
Random forest algorithm;
Dimensionality reduction algorithms;
Gradient boosting algorithm and Ada-Boosting algorithm
Some Examples of
Machine Learning Problems: Machine Learning problems are
abound. They make up core or difficult parts of the software you use on the web
or on your desktop every day. Think of the “do you want to follow” suggestions
on twitter and the speech understanding in Apple’s Siri. Given Below are 10 examples of machine
learning that really ground what machine learning is all about.
a. Spam Detection:
Given email in an inbox, identify those email messages that are spam and those
that are not. Having a model of this problem would allow a program to leave
non-spam emails in the inbox and move spam emails to a spam folder. We should
all be familiar with this example.
b. Credit Card Fraud
Detection: Given credit card transactions for a customer in a
month, identify those transactions that were made by the customer and those
that were not. A program with a model of this decision could refund those
transactions that were fraudulent.
c. Digit Recognition:
Given a zip codes hand written on envelops, identify the digit for each hand
written character. A model of this problem would allow a computer program to
read and understand handwritten zip codes and sort envelops by geographic
region.
d. Speech Understanding:
Given an utterance from a user, identify the specific request made by the user.
A model of this problem would allow a program to understand and make an attempt
to fulfil that request. The iPhone with Siri has this capability.
e. Face Detection:
Given a digital photo album of many hundreds of digital photographs, identify
those photos that include a given person. A model of this decision process
would allow a program to organize photos by person. Some cameras and software
like iPhoto has this capability. We
study an example of face detection from a photo.
f. Product
Recommendation: Given a purchase history for a
customer and a large inventory of products, identify those products in which
that customer will be interested and likely to purchase. A model of this
decision process would allow a program to make recommendations to a customer
and motivate product purchases. Amazon has this capability. Also think of
Facebook, Google Plus and LinkedIn that recommend users to connect with
you after you sign-up.
g. Medical Diagnosis:
Given the symptoms exhibited in a patient and a database of
anonymized patient records, predict whether the patient is likely to have
an illness. A model of this decision problem could be used by a program to
provide decision support to medical professionals.
h.
Stock Trading: Given the current and past price
movements for a stock, determine whether the stock should be bought, held or
sold. A model of this decision problem could provide decision support to
financial analysts.
i. Customer
Segmentation: Given the pattern of behavior by a
user during a trial period and the past behaviors of all users, identify those
users that will convert to the paid version of the product and those that will
not. A model of this decision problem would allow a program to trigger customer
interventions to persuade the customer to covert early or better engage in the
trial.
j.
Shape Detection: Given a user hand drawing a shape on a
touch screen and a database of known shapes, determine which shape the user was
trying to draw. A model of this decision would allow a program to show the
platonic version of that shape the user drew to make crisp diagrams. The Instaviz iPhone app does this.
These 10 examples give
a good sense of what a machine learning problem looks like. There is a corpus
of historic examples, there is a decision that needs to be modeled and a
business or domain benefit to having that decision modeled and efficaciously
made automatically.
Some of these problems
are some of the hardest problems in Artificial Intelligence, such as Natural
Language Processing and Machine Vision (doing things that humans do easily).
Others are still difficult, but are classic examples of machine learning such
as spam detection and credit card fraud detection.
Think about some of
your interactions with online and offline software in the last week. I’m sure
you could easily guess at another ten or twenty example of machine learning you
have directly or indirectly used.
SOME CLASSES OF MACHINE
LEARNING PROBLEMS: This is a valuable skill, because being
good at extracting the essence of a problem will allow you to think effectively
about what data you need and what types of algorithms you should try. There are common classes of problem in
Machine Learning. The problem classes below are archetypes for most of the
problems we refer to when we are doing Machine
Learning.
· Classification:
Data is labelled meaning it is assigned a class, for example spam/non-spam or
fraud/non-fraud. The decision being modelled is to assign labels to new
unlabelled pieces of data. This can be thought of as a discrimination problem,
modelling the differences or similarities between groups.
· Regression:
Data is labelled with a real value (think floating point) rather then a label.
Examples that are easy to understand are time series data like the price of a
stock over time, The decision being modelled is what value to predict for new
unpredicted data.
· Clustering:
Data is not labelled, but can be divided into groups based on similarity and
other measures of natural structure in the data. An example from the above list
would be organising pictures by faces without names, where the human user has
to assign names to groups, like iPhoto on the Mac.
· Rule
Extraction: Data is used as the basis for the extraction of
propositional rules (antecedent/consequent aka if-then). Such rules may, but is typically not directed, meaning
that the methods discover statistically supportable relationships between
attributes in the data, not necessarily involving something that is being
predicted. An example is the discovery of the relationship between the purchase
of beer and diapers (this
is data mining folk-law, true or not, it’s illustrative of the desire and
opportunity).
When you think a
problem is a machine learning problem (a decision problem that needs to be
modelled from data), think next of what type of problem you could phrase it as
easily or what type of outcome the client or requirement is asking for and work
backwards.
Machine Learning and
Deep Learning: Machine Learning and Deep Learning
algorithms are used to train models for data classification. This of the two to
use depends on the kind of problem that is to be solved. Machine Learning and
Deep Learning algorithms are also used in data analysis, processing and
cleansing, more so where the data is unstructured and consists of pictures,
video and voice. This has found critical application in Cyber security in
today’s digital age. Based on its ability to decipher patterns from humungous
data volumes, these models assist in identifying and preventing threats.
Computer Vision: Computer Vision makes
use of algorithms of Machine Learning in simple applications and Deep Learning
for complex applications in detecting and then identifying objects. This is put
to use in the facial recognition feature of smartphones and computers on the
one hand and autonomous vehicles on the other hand. Some other examples of
Computer Vision are bar coding, self-checkout kiosks and medical imaging.
CONCLUSIONS: Machine learning is the ability of a machine
to improve its performance based on previous results. Machine learning methods
enable computers to learn without being explicitly programmed and have multiple
applications, for example The authorswhould like to thank the ae, in the
improvement of data mining algorithms.
ACKNOWLEDGEMENTS:
The
authors of this paper would like to thank the authors of the papers referred
and the designers of the Google images which they made use of .
REFERENCES
1. Machine
Learning: Algorithms, Real-World Applications and Research Directions
Iqbal H. Sarker , SN Computer Science volume 2,
Article number: 160 (2021)
2. Classification of Machine Learning Models - Enjoy Algorithms
https://www.enjoyalgorithms.
com › blog › classification-o.
3. Towards Data Science, , Junn 11, 2018 Machine Learning Classifiers
4. A
Tour of Machine Learning Algorithms; by Jason Brownlee on August 12, 2019 in Machine Learning Algorithms
No comments:
Post a Comment