A fairly recent development in the WEKA software has been the addition of algorithms for multi-instance classification, in particular, methods for ensemble learning. Ensemble classification is a well-known approach for obtaining highly accurate classifiers for single-instance data. This talk will first discuss how randomisation can be applied to multi-instance data by adapting Blockeel et al.'s multi-instance tree inducer to form an ensemble classifier, and then investigate how Maron's diverse density learning method can be used as a weak classifier to form an ensemble using boosting. Experimental results show the benefit of ensemble learning in both cases.
This document provides an introduction to ensemble learning techniques. It defines ensemble learning as combining the predictions of multiple machine learning models. The main ensemble methods described are bagging, boosting, and voting. Bagging involves training models on random subsets of data and combining results by majority vote. Boosting iteratively trains models to focus on misclassified examples from previous models. Voting simply averages the predictions of different model types. The document discusses how these techniques are implemented in scikit-learn and provides examples of decision tree bagging on the Iris dataset.
How Machine Learning Helps Organizations to Work More Efficiently?Tuan Yang
Data is increasing day by day and so is the cost of data storage and handling. However, by understanding the concepts of machine learning one can easily handle the excessive data and can process it in an affordable manner.
The process includes making models by using several kinds of algorithms. If the model is created precisely for certain task, then the organizations have a very wide chance of making use of profitable opportunities and avoiding the risks lurking behind the scenes.
Learn more about:
» Understanding Machine Learning Objectives.
» Data dimensions in Machine Learning.
» Fundamentals of Algorithms and Mapping from Input/Output.
» Parametric and Non-parametric Machine Learning Algorithms.
» Supervised, Unsupervised and Semi-Supervised Learning.
» Estimating Over-fitting and Under-fitting.
» Use Cases.
Machine learning can be used to predict whether a user will purchase a book on an online book store. Features about the user, book, and user-book interactions can be generated and used in a machine learning model. A multi-stage modeling approach could first predict if a user will view a book, and then predict if they will purchase it, with the predicted view probability as an additional feature. Decision trees, logistic regression, or other classification algorithms could be used to build models at each stage. This approach aims to leverage user data to provide personalized book recommendations.
IRJET - A Survey on Machine Learning Algorithms, Techniques and ApplicationsIRJET Journal
This document discusses machine learning algorithms, techniques, and applications. It begins with an introduction to machine learning and different types of learning including supervised learning, unsupervised learning, reinforcement learning, and others. It then groups various machine learning algorithms based on similarities and compares the performance of popular algorithms like Naive Bayes, support vector machines, and decision trees. The document concludes that machine learning researchers aim to design more efficient algorithms that can perform better across different domains.
Hierarchical clustering methods create a hierarchy of clusters based on distance or similarity measures. They do not require specifying the number of clusters k in advance. Hierarchical methods either merge smaller clusters into larger ones (agglomerative) or split larger clusters into smaller ones (divisive) at each step. This continues recursively until all objects are linked or placed into individual clusters.
This document provides an overview of cluster analysis techniques. It discusses the basic concepts of cluster analysis, associated statistics, and how to conduct cluster analysis, including formulating the problem, selecting distance/similarity measures and clustering procedures, deciding the number of clusters, interpreting results, and assessing validity. It covers hierarchical clustering methods like agglomerative and divisive approaches as well as non-hierarchical methods like k-means clustering.
Performance Issue? Machine Learning to the rescue!Maarten Smeets
t can be difficult to determine how to improve performance of microservices. There are many factors you can vary but which factor will be the one having most impact? During this presentation, a method using the random forest machine learning algorithm will be applied in order to help improve performance of a microservice running inside a JVM. Several measures are taken such as thoughput and response times. Java version, JVM supplier, heap, garbage collection algorithm and microservice framework are all varied. Which factor is most important in determining the response time and throughput of the services? The Random Forest algorithm will be introduced to solve this challenge. Not only will this presentation give some useful suggestions for improving the performance of microservices but will also introduce a novel way to take on the challenge of performance tuning which can be applied to other use-cases. This presentation is especially interesting to developers and architects.
This document provides an introduction to ensemble learning techniques. It defines ensemble learning as combining the predictions of multiple machine learning models. The main ensemble methods described are bagging, boosting, and voting. Bagging involves training models on random subsets of data and combining results by majority vote. Boosting iteratively trains models to focus on misclassified examples from previous models. Voting simply averages the predictions of different model types. The document discusses how these techniques are implemented in scikit-learn and provides examples of decision tree bagging on the Iris dataset.
How Machine Learning Helps Organizations to Work More Efficiently?Tuan Yang
Data is increasing day by day and so is the cost of data storage and handling. However, by understanding the concepts of machine learning one can easily handle the excessive data and can process it in an affordable manner.
The process includes making models by using several kinds of algorithms. If the model is created precisely for certain task, then the organizations have a very wide chance of making use of profitable opportunities and avoiding the risks lurking behind the scenes.
Learn more about:
» Understanding Machine Learning Objectives.
» Data dimensions in Machine Learning.
» Fundamentals of Algorithms and Mapping from Input/Output.
» Parametric and Non-parametric Machine Learning Algorithms.
» Supervised, Unsupervised and Semi-Supervised Learning.
» Estimating Over-fitting and Under-fitting.
» Use Cases.
Machine learning can be used to predict whether a user will purchase a book on an online book store. Features about the user, book, and user-book interactions can be generated and used in a machine learning model. A multi-stage modeling approach could first predict if a user will view a book, and then predict if they will purchase it, with the predicted view probability as an additional feature. Decision trees, logistic regression, or other classification algorithms could be used to build models at each stage. This approach aims to leverage user data to provide personalized book recommendations.
IRJET - A Survey on Machine Learning Algorithms, Techniques and ApplicationsIRJET Journal
This document discusses machine learning algorithms, techniques, and applications. It begins with an introduction to machine learning and different types of learning including supervised learning, unsupervised learning, reinforcement learning, and others. It then groups various machine learning algorithms based on similarities and compares the performance of popular algorithms like Naive Bayes, support vector machines, and decision trees. The document concludes that machine learning researchers aim to design more efficient algorithms that can perform better across different domains.
Hierarchical clustering methods create a hierarchy of clusters based on distance or similarity measures. They do not require specifying the number of clusters k in advance. Hierarchical methods either merge smaller clusters into larger ones (agglomerative) or split larger clusters into smaller ones (divisive) at each step. This continues recursively until all objects are linked or placed into individual clusters.
This document provides an overview of cluster analysis techniques. It discusses the basic concepts of cluster analysis, associated statistics, and how to conduct cluster analysis, including formulating the problem, selecting distance/similarity measures and clustering procedures, deciding the number of clusters, interpreting results, and assessing validity. It covers hierarchical clustering methods like agglomerative and divisive approaches as well as non-hierarchical methods like k-means clustering.
Performance Issue? Machine Learning to the rescue!Maarten Smeets
t can be difficult to determine how to improve performance of microservices. There are many factors you can vary but which factor will be the one having most impact? During this presentation, a method using the random forest machine learning algorithm will be applied in order to help improve performance of a microservice running inside a JVM. Several measures are taken such as thoughput and response times. Java version, JVM supplier, heap, garbage collection algorithm and microservice framework are all varied. Which factor is most important in determining the response time and throughput of the services? The Random Forest algorithm will be introduced to solve this challenge. Not only will this presentation give some useful suggestions for improving the performance of microservices but will also introduce a novel way to take on the challenge of performance tuning which can be applied to other use-cases. This presentation is especially interesting to developers and architects.
Initial Experiments on Learning Based Randomized Bin-Picking Allowing Finger...Kensuke Harada
This document summarizes an experiment on learning-based randomized bin-picking that allows finger contact with neighboring objects. The experiment uses linear SVM and random forest models to predict success or failure cases of picking objects based on the distribution of neighboring objects within the swept volume of the robot finger motion. The models were trained on datasets of successful and failed pick attempts. The random forest model achieved over 90% success prediction accuracy, significantly higher than conventional bin-picking methods. The experiment demonstrates that allowing finger contact and using machine learning to predict outcomes can enable automated randomized bin-picking.
This document provides an overview of classification predictive modeling and decision trees. It discusses classification, including binary, multi-class, and multi-label classification. It then describes decision trees, including key terminology like root nodes, interior nodes, and leaf nodes. It explains how decision trees are built and parameters like max_depth and min_samples_leaf. Finally, it compares regression and classification trees and discusses advantages and disadvantages of decision trees.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
This document provides an overview of key concepts in data mining. It discusses how data mining has grown with the increase in digital data and is now a mature discipline. The document outlines different types of data, variables, and learning techniques used in data mining like supervised learning, unsupervised learning, decision trees, clustering, association rule learning, and hidden Markov models. It also compares data mining and process mining, and discusses evaluating the quality of mining results using metrics like confusion matrices and cross-validation.
Data mining Basics and complete description onwordSulman Ahmed
This document discusses data mining and provides examples of its applications. It begins by explaining why data is mined from both commercial and scientific viewpoints in order to discover useful patterns and information. It then discusses some of the challenges of data mining, such as dealing with large datasets, high dimensionality, complex data types, and distributed data sources. The document outlines common data mining tasks like classification, clustering, association rule mining, and regression. It provides real-world examples of how these techniques are used for applications like fraud detection, customer profiling, and scientific discovery.
The document summarizes an Analytics Vidhya meetup event. It discusses that the meetups will occur once a month, with the next one on May 24th. It aims to provide networking and learning around data science, big data, machine learning and IoT. It introduces the volunteer organizers and outlines the agenda, which includes an introduction, discussing the model building lifecycle, data exploration techniques, and modeling techniques like logistic regression, decision trees, random forests, and SVMs. It provides details on practicing these techniques by predicting survival on the Titanic dataset.
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The document discusses two problems related to real-time activity recognition using data from wearable sensors and mobile phones. For problem 1 of developing an algorithm to recognize exercises from a raw sensor data stream, the solution involves a two-phase learning and recognition process using techniques like filtering, time-windowing, feature extraction and selection, and classification models. For problem 2 of enabling real-time recognition on mobile phones, the document recommends using Android and Java APIs to receive Bluetooth sensor data, train models on servers, and locally recognize activities on phones for efficiency. Key challenges discussed include energy usage, response time, and developing flexible models for different users.
Multi-class Classification on Riemannian Manifolds for Video SurveillanceDiego Tosato
In video surveillance, classification of visual data can be very hard due to the scarce resolution and the noise characterizing the sensors data. In this paper, we propose a novel feature, the ARray of COvariances (ARCO), and a multi-class classification framework operating on Riemannian manifolds. ARCO is composed by a structure of covariance matrices of image features, able to extract information from data at prohibitive low resolutions. The proposed classification framework consists in instantiating a new multi-class boosting method, working on the manifoldof symmetric positive definite d×d (covariance) matrices. As practical applications, we consider different surveillance tasks, such as head pose classification and pedestrian detection, providing novel state-of-the-art performances on standard datasets.
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This document provides an overview of business intelligence and data warehousing topics. It discusses the ID3 algorithm for building decision trees from datasets, the WEKA data mining software suite, and applications of web mining for business. The ID3 algorithm attempts to create the smallest possible decision tree using information theory. WEKA contains tools for data pre-processing, classification, clustering, and more. Web mining techniques can be used to generate user profiles, target internet advertising, detect fraud, and improve web search capabilities.
This document provides an overview of machine learning techniques for classification with imbalanced data. It discusses challenges with imbalanced datasets like most classifiers being biased towards the majority class. It then summarizes techniques for dealing with imbalanced data, including random over/under sampling, SMOTE, cost-sensitive classification, and collecting more data. [/SUMMARY]
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- Simulations model and imitate real-world systems through mathematical relationships, allowing researchers to manipulate variables and observe outcomes. Virtual worlds are persistent online spaces created and shaped by user interactions.
- They allow for prediction, understanding, explanation, and safe exploration of concepts. Researchers have control and visibility while being economical. However, they are not a replacement for real-world experiences.
- Applications include modeling real-life scenarios, collecting large data sets, studying human interaction over time, and exploring sensitive issues. However, challenges include ensuring common
Software can be used to speed up R&D into sustainable solutions such as alternative energy (batteries, fuel cells, biomass conversion), catalysts, and eljminiating environmental toxins. The presentation gives an overview of the various methods and illustrates their applicaiton with case studies.
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Distance-based bias in model-directed optimization of additively decomposable...Martin Pelikan
For many optimization problems it is possible to define a distance metric between problem variables that correlates with the likelihood and strength of interactions between the variables. For example, one may define a metric so that the dependencies between variables that are closer to each other with respect to the metric are expected to be stronger than the dependencies between variables that are further apart. The purpose of this paper is to describe a method that combines such a problem-specific distance metric with information mined from probabilistic models obtained in previous runs of estimation of distribution algorithms with the goal of solving future problem instances of similar type with increased speed, accuracy and reliability. While the focus of the paper is on additively decomposable problems and the hierarchical Bayesian optimization algorithm, it should be straightforward to generalize the approach to other model-directed optimization techniques and other problem classes. Compared to other techniques for learning from experience put forward in the past, the proposed technique is both more practical and more broadly applicable.
Data Mining Module 3 Business Analtics..pdfJayanti Pande
Business Analytics Paper 2
| Data Mining | RTMNU Nagpur University MBA | Module 3
| Decision Trees and Decision Rules | By Jayanti Pande | ProNotesJRP | JRP Notes
Spectral Learning Methods for Finite State Machines with Applications to Na...LARCA UPC
The document summarizes a spectral learning method for probabilistic finite-state machines (FSMs). It introduces observable operator models that represent probabilistic transducers using conditional probabilities between inputs, outputs, and hidden states. A key contribution is a spectral algorithm that learns the parameters of these models from data in linear time, with theoretical PAC-style guarantees. Experimental results on synthetic data show the method outperforms baselines like HMMs and k-HMMs on learning tasks.
Information networks are a popular way to represent information, especially in domains where the emphasis lies on the structural relationships between the entities rather than their features. Notable examples are online social networks and road networks. This special focus on network topology has led to the development of specialized graph databases. However, few of these databases offer a high-level declarative interface suited for analyzing information networks.
In this talk I present our work on developing a query language for analyzing networks. I will focus on the general principles we followed in the design of this language, and the main challenges related to developing it into a scalable tool for network analysis.
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Activity Monitoring Using Wearable Sensors and Smart PhoneDrAhmedZoha
The document discusses two problems related to real-time activity recognition using data from wearable sensors and mobile phones. For problem 1 of developing an algorithm to recognize exercises from a raw sensor data stream, the solution involves a two-phase learning and recognition process using techniques like filtering, time-windowing, feature extraction and selection, and classification models. For problem 2 of enabling real-time recognition on mobile phones, the document recommends using Android and Java APIs to receive Bluetooth sensor data, train models on servers, and locally recognize activities on phones for efficiency. Key challenges discussed include energy usage, response time, and developing flexible models for different users.
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Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Full-RAG: A modern architecture for hyper-personalization
Experiments with Randomisation and Boosting for Multi-instance Classification
1. Experiments with Randomisation
and Boosting for Multi-instance
Classification
Luke Bjerring, James Foulds, Eibe Frank
University of Waikato
September 13, 2011