Machine learning techniques can be used to enable computers to learn from data and perform tasks. Some key techniques discussed in the document include decision tree learning, artificial neural networks, Bayesian learning, support vector machines, genetic algorithms, graph-based learning, reinforcement learning, and pattern recognition. Each technique has its own strengths and applications.
The AML group carries out both theoretical and experimental work on developing and applying new machine learning techniques for solving various application problems.
Identification of Relevant Sections in Web Pages Using a Machine Learning App...Jerrin George
A brief introduction about Machine Learning, Supervised and Unsupervised Learning, and Support Vector Machines.
Application of a Supervised Algorithm to identify relevant sections of webpages obtained in search results using an SVM.
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.
The AML group carries out both theoretical and experimental work on developing and applying new machine learning techniques for solving various application problems.
Identification of Relevant Sections in Web Pages Using a Machine Learning App...Jerrin George
A brief introduction about Machine Learning, Supervised and Unsupervised Learning, and Support Vector Machines.
Application of a Supervised Algorithm to identify relevant sections of webpages obtained in search results using an SVM.
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.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
In this Lunch & Learn session, Chirag Jain gives us a friendly & gentle introduction to Machine Learning & walks through High-Level Learning frameworks using Linear Classifiers.
Machine learning (ML) and natural language processing (NLP)Nikola Milosevic
Short introduction on natural language processing (NLP) and machine learning (ML). Speaks about sub-areas of artificial inteligence and then mainly focuses on the sub-areas of machine learning and natural language processing. Explains the process of data mining from high perspective
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.
Provides a brief overview of what machine learning is, how it works (theory), how to prepare data for a machine learning problem, an example case study, and additional resources.
The Presentation answers various questions such as what is machine learning, how machine learning works, the difference between artificial intelligence, machine learning, deep learning, types of machine learning, and its applications.
Machine learning with Big Data power point presentationDavid Raj Kanthi
This is an article made form the articles of IEEE published in the year 2017
The following presentation has the slides for the Title called the
Machine Learning with Big data. that following presentation which has the challenges and approaches of machine learning with big data.
The integration of the Big Data with Machine Learning has so many challenges that Big data has and what is the approach made by the machine learning mechanism for those challenges.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
In this Lunch & Learn session, Chirag Jain gives us a friendly & gentle introduction to Machine Learning & walks through High-Level Learning frameworks using Linear Classifiers.
Machine learning (ML) and natural language processing (NLP)Nikola Milosevic
Short introduction on natural language processing (NLP) and machine learning (ML). Speaks about sub-areas of artificial inteligence and then mainly focuses on the sub-areas of machine learning and natural language processing. Explains the process of data mining from high perspective
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.
Provides a brief overview of what machine learning is, how it works (theory), how to prepare data for a machine learning problem, an example case study, and additional resources.
The Presentation answers various questions such as what is machine learning, how machine learning works, the difference between artificial intelligence, machine learning, deep learning, types of machine learning, and its applications.
Machine learning with Big Data power point presentationDavid Raj Kanthi
This is an article made form the articles of IEEE published in the year 2017
The following presentation has the slides for the Title called the
Machine Learning with Big data. that following presentation which has the challenges and approaches of machine learning with big data.
The integration of the Big Data with Machine Learning has so many challenges that Big data has and what is the approach made by the machine learning mechanism for those challenges.
Un servidor es un tipo de software que se encarga de realizar una serie de tareas en nombre de usuarios, también puede definirse como un ordenador físico en el cual funciona ese software, es decir una máquina cuyo propósito es proveer datos de tal modo que otras máquinas puedan utilizar dichos datos.
This slide gives brief overview of supervised, unsupervised and reinforcement learning. Algorithms discussed are Naive Bayes, K nearest neighbour, SVM,decision tree, Markov model.
Difference between regression and classification. difference between supervised and reinforcement, iterative functioning of Markov model and machine learning applications.
Machine learning(ML) is the scientific study of algorithms and statistical models that computer systems used to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “Training Data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in the applications of email filtering, detection of network intruders and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, Machine learning is the study of computer systems that learn from data and experience. It is applied in an incredibly wide variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential customer of machine learning.
In a world of data explosion, the rate of data generation and consumption is on the increasing side, there comes the buzzword - Big Data.
Big Data is the concept of fast-moving, large-volume data in varying dimensions (sources) and
highly unpredicted sources.
The 4Vs of Big Data
● Volume - Scale of Data
● Velocity - Analysis of Streaming Data
● Variety - Different forms of Data
● Veracity - Uncertainty of Data
With increasing data availability, the new trend in the industry demands not just data collection,
but making ample sense of acquired data - thereby, the concept of Data Analytics.
Taking it a step further to further make a futuristic prediction and realistic inferences - the concept
of Machine Learning.
A blend of both gives a robust analysis of data for the past, now and the future.
There is a thin line between data analytics and Machine learning which becomes very obvious
when you dig deep.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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USING ONTOLOGIES TO IMPROVE DOCUMENT CLASSIFICATION WITH TRANSDUCTIVE SUPPORT...IJDKP
Many applications of automatic document classification require learning accurately with little training
data. The semi-supervised classification technique uses labeled and unlabeled data for training. This
technique has shown to be effective in some cases; however, the use of unlabeled data is not always
beneficial.
On the other hand, the emergence of web technologies has originated the collaborative development of
ontologies. In this paper, we propose the use of ontologies in order to improve the accuracy and efficiency
of the semi-supervised document classification.
We used support vector machines, which is one of the most effective algorithms that have been studied for
text. Our algorithm enhances the performance of transductive support vector machines through the use of
ontologies. We report experimental results applying our algorithm to three different datasets. Our
experiments show an increment of accuracy of 4% on average and up to 20%, in comparison with the
traditional semi-supervised model.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
2. Introduction
• Machine Learning is considered as a subfield of
Artificial Intelligence and it is concerned with the
development of techniques and methods which enable
the computer to learn. In simple terms, it is considered
the science of development of algorithms which enable
the machine to learn and perform tasks and activities.
• Limitations
– Algorithms/Techniques vary learning over large datasets
and often misinterpret.
– Learning is based on the data which is provided.
– Machine learning algorithms suffer from the possibility of
overgeneralization
3. Decision-Tree Learning
• The machine learning technique for inducing a decision
tree from data is called decision tree learning, or
(colloquially) decision trees. The major advantage of
decision trees is its ability to interpret a trained model.
Decision trees also works with numerical data as input,
since they find the margin that maximizes information
gain. Their ability to mix categorical and numerical data
is another advantage.
• Inductive Bias: Shorter trees are preferred over larger
ones.
• Occam's razor: Prefer the simplest hypothesis which
fits the data.
4. Artificial Neural Networks
• An artificial neural network (ANN), often just called a "neural network" (NN),
is a mathematical model or computational model based on biological neural
networks. It consists of an interconnected group of artificial neurons and
processes information using a connectionist approach to computation. In
most cases an ANN is an adaptive system that changes its structure based on
external or internal information that flows through the network during the
learning phase. The greatest advantage of ANNs is their ability to be used as
an arbitrary function approximation mechanism which 'learns' from observed
data. However, using them is not so straightforward and a relatively good
understanding of the underlying theory is essential. Examples Include
application areas include game-playing and decision making (backgammon,
chess, racing), pattern recognition (face identification, object recognition and
more), sequence recognition (gesture, speech, handwritten text recognition),
medical diagnosis, financial applications (automated trading systems), data
mining (knowledge discovery), visualization and e-mail spam filtering.
5. Bayesian Learning
• Bayesian Learning is a probabilistic approach to learning and
inference. It is based on the assumption that the quantities of
interest are governed by probability distributions. It is attractive
because in theory it can arrive at optimal decisions. It provides a
quantitative approach to weighing the evidence supporting
alternative hypotheses. Bayesian learning has been successfully
applied to Data mining, Robotics, Signal processing, Bioinformatics,
Text analysis (spam filters), and graphics. Bayesian methods can be
used to determine the most probable hypothesis given the data,
maximum a posteriori (MAP) hypothesis. A naive Bayes classifier is
a simple probabilistic classifier based on applying Bayes' theorem
with strong (naive) independence assumptions.
• Bayes theorem is
P (H|X) = P(X|H) P(H) / P(X)
6. Support Vector Machines
• Support vector machines (SVMs) are a set of related supervised
learning methods used for classification and regression. They belong
to a family of generalized linear classifiers. A special property of
SVMs is that they simultaneously minimize the empirical
classification error and maximize the geometric margin; hence they
are also known as maximum margin classifiers. Support vector
machines map input vectors to a higher dimensional space where a
maximal separating hyperplane is constructed. Two parallel
hyperplanes are constructed on each side of the hyperplane that
separates the data. The separating hyperplane is the hyperplane that
maximizes the distance between the two parallel hyperplanes. An
assumption is made that the larger the margin or distance between
these parallel hyperplanes the better the generalization error of the
classifier will be.
7. Genetic Algorithms
• Genetic algorithm (GA) is a search technique used in computing to find exact or
approximate solutions to optimization and search problems. Genetic algorithms
are categorized as global search heuristics. Genetic algorithms are a particular
class of evolutionary algorithms (also known as evolutionary computation) that
use techniques inspired by evolutionary biology such as inheritance, mutation,
selection, and crossover (also called recombination). The following steps describe
the application of a genetic algorithm: Start with an initial population (e.g.
random) of candidate solutions, repeatedly apply a number of genetic operators to
generate a new population and denote the best individual of the last generation
(population) as the solution. The operators that a genetic algorithm uses are:
• Reproduction: Select individuals with higher fitness than others to reproduce so
that their children are found in the next generation. Unfit individuals die with
higher probability than fitter ones.
• Crossover: Combine two reproduced individuals so that their children are copies in
the next generation.
• Mutation: Probabilistic change of part of an individual.
• Genetic algorithms are simple to implement, but their behavior is difficult to
understand.
8. Graph-based Learning
• Graph-based relational learning (GBRL) is the
task of finding novel, useful, and
understandable graph-theoretic patterns in a
graph representation of data. Graph-based
data representation is becoming increasingly
more commonplace, as graphs can represent
some kinds of data more efficiently than
relational tables.
9. Reinforcement Learning
• Reinforcement learning (RL) is learning by
interacting with an environment. An RL agent
learns from the consequences of its actions,
rather than from being explicitly taught and it
selects its actions on basis of its past
experiences (exploitation) and also by new
choices (exploration), which is essentially trial
and error learning.
10. Pattern Recognition
• Pattern recognition aims to classify data (patterns)
based on either a priori knowledge or on statistical
information extracted from the patterns. The patterns
to be classified are usually groups of measurements or
observations, defining points in an appropriate
multidimensional space. Typical applications are
automatic speech recognition, classification of text into
several categories (e.g. spam/non-spam email
messages), the automatic recognition of handwritten
postal codes on postal envelopes, or the automatic
recognition of images of human faces.
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