Machine learning is a technology design to build intelligent systems. These systems also have the ability to learn from past experience or analyze historical data. It provides results according to its experience.
Alpavdin defines Machine Learning as-
“Optimizing a performance criterion using example data and past experience”.
Data is the key concept of machine learning. We can also apply its algorithms on data to identify hidden patterns and gain insights. These patterns and gained knowledge help systems to learn and improve their performance.
Machine learning technology involves both statistics and computer science. Statistics allows one to draw inferences from the given data. To implement efficient algorithms we can also use computer science. It represents the required model, and evaluate the performance of the model.
This was part of my inaugural lecture of Summer Internship on Machine Learning at NMAM Institute of Technology, Nitte on 7th June, 2018. A lot more than what was on this presentation was discussed. We spoke on the ethics of choices we make as developers, socio-cultural impact of AI and ML and the political repercussions of deploying ML and AI.
The world today is evolving and so are the needs and requirements of people. Furthermore, we are witnessing a fourth industrial revolution of data.
Machine Learning has revolutionized industries like medicine, healthcare, manufacturing, banking, and several other industries. Therefore, Machine Learning has become an essential part of modern industry.
Hot Topics in Machine Learning For Research and thesisWriteMyThesis
Machine Learning and its subsequent fields have undergone tremendous growth in the past few years. It has a number of potential applications and is being used in different fields. A lot of research work is going on in this field. For more information, check out the PPT details...
This was part of my inaugural lecture of Summer Internship on Machine Learning at NMAM Institute of Technology, Nitte on 7th June, 2018. A lot more than what was on this presentation was discussed. We spoke on the ethics of choices we make as developers, socio-cultural impact of AI and ML and the political repercussions of deploying ML and AI.
The world today is evolving and so are the needs and requirements of people. Furthermore, we are witnessing a fourth industrial revolution of data.
Machine Learning has revolutionized industries like medicine, healthcare, manufacturing, banking, and several other industries. Therefore, Machine Learning has become an essential part of modern industry.
Hot Topics in Machine Learning For Research and thesisWriteMyThesis
Machine Learning and its subsequent fields have undergone tremendous growth in the past few years. It has a number of potential applications and is being used in different fields. A lot of research work is going on in this field. For more information, check out the PPT details...
Machine Learning jobs are one of the top emerging jobs in the industry currently, and standing out during an interview is key for landing your desired job. Here are some Machine Learning interview questions you should know about, if you plan to build a successful career in the field.
In the past few years, India has witnessed exponential growth in the sector of Data Science. With the advent of digital transformation in businesses, the demand for data scientists is boosting every day with a ton of job opportunities machine learning course in mumbai’machine learning course in mumbais lying in their path. Boston Institute of Analytics provides data science courses in Mumbai. They train students under experienced industry professionals and make them industry ready. To know more about their courses check out their website https://www.biaclassroom.com/courses.
An introductory course on building ML applications with primary focus on supervised learning. Covers the typical ML application cycle - Problem formulation, data definitions, offline modeling, platform design. Also, includes key tenets for building applications.
Note: This is an old slide deck. The content on building internal ML platforms is a bit outdated and slides on the model choices do not include deep learning models.
Classification of Machine Learning AlgorithmsAM Publications
The goal of various machine learning algorithms is to device learning algorithms that learns automatically without any human intervention or assistance. The emphasis of machine learning is on automatic methods. Supervised Learning, unsupervised learning and reinforcement learning are discussed in this paper. Machine learning is the core area of Artificial Intelligence. Although a subarea of AI, machine learning also intersects broadly with other fields, especially statistics, but also mathematics, physics, theoretical computer science and more.
Machine Learning jobs are one of the top emerging jobs in the industry currently, and standing out during an interview is key for landing your desired job. Here are some Machine Learning interview questions you should know about, if you plan to build a successful career in the field.
In the past few years, India has witnessed exponential growth in the sector of Data Science. With the advent of digital transformation in businesses, the demand for data scientists is boosting every day with a ton of job opportunities machine learning course in mumbai’machine learning course in mumbais lying in their path. Boston Institute of Analytics provides data science courses in Mumbai. They train students under experienced industry professionals and make them industry ready. To know more about their courses check out their website https://www.biaclassroom.com/courses.
An introductory course on building ML applications with primary focus on supervised learning. Covers the typical ML application cycle - Problem formulation, data definitions, offline modeling, platform design. Also, includes key tenets for building applications.
Note: This is an old slide deck. The content on building internal ML platforms is a bit outdated and slides on the model choices do not include deep learning models.
Classification of Machine Learning AlgorithmsAM Publications
The goal of various machine learning algorithms is to device learning algorithms that learns automatically without any human intervention or assistance. The emphasis of machine learning is on automatic methods. Supervised Learning, unsupervised learning and reinforcement learning are discussed in this paper. Machine learning is the core area of Artificial Intelligence. Although a subarea of AI, machine learning also intersects broadly with other fields, especially statistics, but also mathematics, physics, theoretical computer science and more.
what-is-machine-learning-and-its-importance-in-todays-world.pdfTemok IT Services
Machine Learning is an AI method for teaching computers to learn from their mistakes. Machine learning algorithms can “learn” data directly from data without using an equation as a model by employing computational methods.
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A large number of techniques has been developed so far to tell the diversity of machine learning. Machine learning is categorized into supervised, unsupervised and reinforcement learning .Every instance in given data-set used by Machine learning algorithms is represented same set of features .On basis of label of instances it is divided into category. In this review paper our main focus is on Supervised, unsupervised learning techniques and its performance parameters.
Overview of Machine learning concepts – Over fitting and train/test splits, Types of Machine learning – Supervised, Unsupervised, Reinforced learning, Introduction to Bayes Theorem, Linear Regression- model assumptions, regularization (lasso, ridge, elastic net), Classification and Regression algorithms- Naïve Bayes, K-Nearest Neighbors, logistic regression, support vector machines (SVM), decision trees, and random forest, Classification Errors..
Machine Learning is a fascinating field that has been making headlines for its incredible advancements in recent years. Whether you're a tech enthusiast or just curious about how machines can learn, this article will provide you with a simple and easy-to-understand overview of some key Machine Learning concepts. Think of it as your first step towards a Machine Learning Complete Course!
100-Concepts-of-AI By Anupama Kate .pptxAnupama Kate
🔍 Dive into the Core of AI with Our Latest SlideShare! Explore the essential paradigms of machine learning: Supervised, Semi-Supervised, and Unsupervised Learning. Understand how these frameworks shape AI applications and drive innovation across industries. Perfect for professionals eager to enhance their AI knowledge and harness the full potential of machine learning technologies. #MachineLearning #AI #DataScience #TechInnovation
Artificial neural network for machine learninggrinu
An Artificial Neurol Network (ANN) is a computational model. It is based on the structure and functions of biological neural networks. It works like the way human brain processes information. ANN includes a large number of connected processing units that work together to process information. They also generate meaningful results from it.
The 21st century; oh, what a time to be alive! With the world at your fingertips, it is easier than ever to dream big. But the question is- where to begin? With a wide range of programming languages to choose from to begin with, this article isn’t a gimmick for Python. Through this piece of writing, we hope to open you up to the realities of the world of Python. We will let you know the reasons why should I learn Python programming, what are the benefits of learning Python, what can I do with Python programming language and how can I start a career in Python Programming.
Python standard library & list of important librariesgrinu
We know that a module is a file with some Python code, and a package is a directory for sub packages and modules. But the line between a package and a Python library is quite blurred.
A Python library is a reusable chunk of code that you may want to include in your programs/ projects. Compared to languages like C++ or C, a Python libraries do not pertain to any specific context in Python. Here, a ‘library’ loosely describes a collection of core modules. Essentially, then, a library is a collection of modules. A package is a library that can be installed using a package manager like rubygems or npm.
Data Mining is a set of method that applies to large and complex databases. This is to eliminate the randomness and discover the hidden pattern. As these data mining methods are almost always computationally intensive. We use data mining tools, methodologies, and theories for revealing patterns in data. There are too many driving forces present. And, this is the reason why data mining has become such an important area of study.
In this Python Machine Learning Tutorial, Machine Learning also termed ML. It is a subset of AI (Artificial Intelligence) and aims to grants computers the ability to learn by making use of statistical techniques. It deals with algorithms that can look at data to learn from it and make predictions.
A Heuristic is a technique to solve a problem faster than classic methods, or to find an approximate solution when classic methods cannot. This is a kind of a shortcut as we often trade one of optimality, completeness, accuracy, or precision for speed. A Heuristic (or a heuristic function) takes a look at search algorithms. At each branching step, it evaluates the available information and makes a decision on which branch to follow.
Wee are discussing 20 best applications of deep learning with Python, that you must know. Let’s discuss them one by one:
i. Restoring Color in B&W Photos and Videos
With Deep Learning, it is possible to restore color in black and white photos and videos. This can give a new life to such media. The ACM Digital Library is one such project that colorizes grayscale images combining global priors and local image features. This is based on Convolutional Neural Networks.
The Deep Learning network learns patterns that naturally occur within photos. This includes blue skies, white and gray clouds, and the greens of grasses. It uses past experience to learn this. Although sometimes, it can make mistakes, it is efficient and accurate most of the times.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
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June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
2. Contents
Machine Learning Tutorial – What is Machine Learning? .......................................3
Machine Learning Tutorial – Data Mining vs Machine Learning.............................4
Machine Learning Tutorial – Types of Machine Learning .......................................4
i. Supervised learning................................................................................................5
ii. Unsupervised Learning..........................................................................................5
iii. Reinforcement Learning.......................................................................................5
Machine Learning Tutorial – Machine Learning Approaches ..................................6
i. Decision Tree Learning .......................................................................................6
ii. Support Vector Machines ....................................................................................6
iii. Association Rule Learning ................................................................................7
iv. Artificial Neural Networks (ANN)....................................................................7
v. Inductive Logic Programming............................................................................7
vi. Reinforcement Learning....................................................................................7
vii. Clustering..........................................................................................................8
viii. Similarity and Metric Learning .......................................................................8
ix. Bayesian Networks ............................................................................................8
x. Representation Learning.....................................................................................8
xi. Sparse Dictionary Learning...............................................................................9
Conclusion .................................................................................................................9
3. Machine Learning Tutorial for Beginners – Learn Machine Learning
Machine Learning Tutorial – What is Machine Learning?
Machine learning is a technology design to build intelligent systems. These systems also have the ability
to learn from past experience or analyze historical data. It provides results according to its experience.
Alpavdin defines Machine Learning as-
“Optimizing a performance criterion using example data and past experience”.
Data is the key concept of machine learning. We can also apply its algorithms on data to identify hidden
patterns and gain insights. These patterns and gained knowledge help systems to learn and improve their
performance.
Machine learning technology involves both statistics and computer science. Statistics allows one to
draw inferences from the given data. To implement efficient algorithms we can also use computer
science. It represents the required model, and evaluate the performance of the model.
Machine learning involves some advanced statistical concepts such as modeling and
optimization. Modeling refers to the conditions or probability distribution for the given sample
data. Optimization also includes techniques used to find the most appropriate parameters for the given
set of data.
The knowledge helps systems to learn and improve their performance. We can use Modern Learning
technology in several areas such as artificial neural networks, data mining, web ranking etc.
4. Machine Learning Tutorial – Data Mining vs Machine Learning
In Big Data analytics, data mining and machine learning are the two most commonly used
techniques. Learners get confused between the two but they are two different approaches used for two
different purposes.
Here, in this part of Machine Learning Tutorial, we will see the difference between data mining and
machine learning.
Data mining is the process of identifying patterns in large amounts of data to extract useful
information from those patterns. It may include techniques of artificial intelligence, machine
learning, neural networks, and statistics. The basis of data mining is real world data. It may have
taken inspiration and techniques from machine learning and statistics but is put to different ends. A
person carries out data mining in a specific situation on a particular data set. The goal is to leverage
the power of the various pattern recognition techniques of machine learning.
But machine learning process is an approach to developing artificial intelligence. We use
Machine Learning algorithm to develop the new algorithms and techniques. These allow the
machine to learn from the analyzed data or with experience. Most tasks that need intelligence must
have an ability to induce new knowledge from experiences. Thus, a large area within AI is machine
learning. This involves the study of algorithms that can extract information without on-line human
guidance.
Machine Learning relates to the study, design, and development of the algorithms. These give
computers the capability to learn without being explicitly programmed. Data Mining starts with
unstructured data and tries to extract knowledge or interesting patterns. During this process, we use
machine Learning algorithms.
Machine Learning Tutorial – Types of Machine Learning
In this Machine Learning Tutorial, we can organize Machine learning algorithms into the taxonomy,
based on the desired outcome of the algorithm. Common algorithm types include:
5. i. Supervised learning
In this, we can present the computer with example inputs and their desired outputs, given by a “teacher”.
Its goal is to learn a general rule that maps inputs to outputs. Spam filtering is an example of supervised
learning. In particular classification, the learning algorithm is present with email messages labeled as
“spam” or “not spam”. This is to produce a computer program that labels unseen messages as either spam
or not. The classification problem is another standard formulation of the supervised learning task. Here
the learner needs to learn a function which maps a vector into one of the several classes. This he can do
by looking at several input-output examples of the function.
ii. Unsupervised Learning
In this, no labels are given to the learning algorithm, leaving it on its own to groups of similar inputs
(clustering), density estimates or projections of high-dimensional data that can be visualized effectively.
Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an
end. Topic modeling is an example of unsupervised learning, where a program is given a list of human
language documents and is tasked to find out which documents cover similar topics.
Here, learning takes places by detecting regularities in input data and developing patterns based on these
deductions. Regularities are observed for repetitive occurrence of a pattern. The more frequently
occurring pattern is used to make predictions. This approach is also called the density estimation
approach. Several methods like clustering can be used for density estimation.
iii. Reinforcement Learning
6. In this, a computer program interacts with a dynamic environment. In this, it must perform a certain goal,
without a teacher explicit telling it whether it has come close to its goal or not.
Let us consider the case of robotic navigation. A robot can make very precise movements to perform a
task. Yet, the robot has to learn to perform these movements through repeated tests. It applies the
knowledge gained from this to improve its efficiency. This is the basis of reinforced learning. In robotic
navigation, and other similar systems, such as a self-driving car, sensor doors the output is not restricted
to a single action. It may contain a sequence of actions.
Machine Learning Tutorial – Machine Learning Approaches
Let us see some most common machine learning approaches:
Machine Learning Tutorial – Machine Learning Approaches
i. Decision Tree Learning
In Decision tree learning, the predictive model used is a decision tree. It maps observations about an
item to conclusions about the item’s target value. This type of learning is generally used in the field of
data mining and machine learning. These trees are also referred as the regression or classification trees.
A decision tree can also provide graphical or implicit representation for decision-making problems.
ii. Support Vector Machines
7. These are sets of related supervised learning methods that you can use for classification and regression.
An SVM training algorithm builds a model that predicts whether a new example falls into one category or
the other.
Support vector machines (SVM) cover both linear and nonlinear classifiers. To classify two-dimensional
training data into two groups, you can use a scatter plot of the two attributes. Using SVM, you can
represent the plotting positions with two different labels or colors that identify the two classes.
iii. Association Rule Learning
You can use it for discovering interesting relations between variables in large databases. This rule is
generally applied to sales data, to find an association among sales of different items. This rule predicts
that if a customer buys an item X, then there are chances, the customer will also buy an item Y.
iv. Artificial Neural Networks (ANN)
An artificial neural network (ANN) learning algorithm is usually called “neural network” (NN). It
has inspired by the structure and functional aspects of biological neural networks. Computations structure
for an interconnected group of artificial neurons. It processes information using a connectionist approach
to computation. Modern neural networks are non-linear statistical data modeling tools. One uses ANN to
model complex relationships between inputs and outputs and to find patterns in data. They also capture
the statistical structure in an unknown joint probability distribution between observed variables.
Artificial Neural Networks (ANN) are intensive methods of computation to find patterns in data sets that
are very large. And it contains many explanatory variables that standard method like multiple regression
is impractical. When the outputs are continuous variables, neural networks can operate like multiple
regressions. They can act like classifications when the outputs are categorical.
v. Inductive Logic Programming
An Inductive logic programming (ILP) is an approach to rule learning using logic programming. It is a
uniform representation of input examples, background knowledge, and hypothesis. An ILP system will
also derive a hypothesized logic program which entails all the positive and none of the negative examples.
Inductive programming considers programming languages for representing hypotheses like functional
programs.
In inductive techniques, we also use a training phase to develop a model, which summarizes the relations
between the variables. We then apply this model to new data to deduce a classification or prediction from
them in inductive techniques.
vi. Reinforcement Learning
The Reinforcement learning is how an agent ought to take actions in an environment to maximize some
notion of long-term reward. It finds a policy that maps states of the world to the actions the agent ought to
take in those states. In it, there is never correct input/output pairs are never present nor sub-optimal
actions correct. This creates the difference between reinforcement learning and supervised learning. The
reinforced learning identifies actions in particular situation to maximize the output of a system.
8. The basis of the reinforced learning is the trial-and-error approach. The learning process takes place by
discovering a learning problem instead of a method. We can also divide a complete
reinforced learning process as:
Sensing the problem
Taking appropriate actions
Goals of actions
vii. Clustering
Cluster analysis is the assignment of a set of observations into subsets called clusters. The observations
within the same cluster are similar according to some predesignated criteria. And observations drawn
from different clusters are dissimilar. Different clustering techniques also make different
assumptions about the structure of the data. Similarity metric defines these techniques. And then,
evaluated by internal compactness and separation between different clusters. Other methods depend on
estimated density and graph connectivity. Clustering is a method of unsupervised learning and it is also a
common technique for statistical data analysis.
viii. Similarity and Metric Learning
In this problem, we give pairs of examples to the learning machine that are similar and pairs of less
similar objects. It then learns a similarity function (or a distance metric function) that can predict if new
objects are similar. Sometimes we can also use it in Recommendation systems.
ix. Bayesian Networks
A Bayesian network or belief network or directed acyclic graphical model. It is a probabilistic graphical
model to represent random variables with conditional independencies. This is often done via directed
acyclic graph (DAG). For example, it could represent the probabilistic relationships between diseases
and symptoms. Given symptoms, we can also use the Bayesian network to compute the probabilities of
the presence of various diseases. Efficient algorithms exist that perform inference and learning.
Bayes’ theorem is one of the most important results in probability theory. It concerns the inversion of
probabilities and relates, for two events A and B, the conditional probability of A given B to the
conditional probability of B given A:
P (A/B) = P (B/A)*P (A)/P (B)
By using the Bayes’ theorem, a user can also construct DAG, relating the dependent variable to the
independent variables. This graph developed is the Bayesian network.
x. Representation Learning
The representation of data is one of the key factors that can affect the performance of the machine
learning method. We use representation learning algorithms to represent data in a better format. The aim
of representation learning algorithms is to store input information and also transform it into a form to
make it more useful.
9. Representation learning algorithms often attempt to preserve the information in their input. It transforms
it in a way that makes it useful, often as a pre-processing step before performing classification or
predictions. This also allows reconstructing the inputs coming from the unknown data generating the
distribution. Here it is not being necessary to be faithful for configurations that are implausible under that
distribution.
xi. Sparse Dictionary Learning
In this method, we represent a datum as a linear combination of basis functions and then assume the
coefficients to be sparse. Let x be a d-dimensional datum, D be add by n matrix, where each column
of D represents a basis function. r is the coefficient which represents x using D. In maths terms, sparse
dictionary learning means the following
Here r is sparse. Generally speaking, we assume n to be larger than d to allow the freedom for a sparse
representation.
We can also apply Sparse dictionary learning in several contexts. In classification, the problem is to
determine which prior classes unseen datum belongs to. Suppose when we develop a dictionary for each
class. Then we associate a new datum with the class such that it can represent it by the corresponding
dictionary. Sparse dictionary learning also finds application in image de-noising. The key idea is that we
can represent a clean image path by an image dictionary, but the noise cannot.
So, this was all about Machine Learning Tutorial. Hope you like our explanation.
Conclusion
Hence, in this Machine Learning Tutorial, we studied Machine learning also gets change because of the
evolution of new computing technologies. Earlier the machine learning was the theory that computers can
learn without being programmed to perform specific tasks but now the researchers interested in artificial
intelligence wanted to see if computers could learn from data. They learn from previous computations to
produce reliable decisions and results. It’s a science that’s not new – but one that’s gaining fresh
momentum.