Comparison Study of Decision Tree Ensembles for RegressionSeonho Park
Nowadays, decision tree ensemble methods are widely used for solving classification and regression problem due to their rigorousness and robustness. To compare with classification, the performance in regression problem so far has not been yet addressed in detail. In this presentation, we review the state-of-art decision tree ensemble methodology in scikit-learn and xgboost for regression. Also, empirical study results are illustrated to compare their performance and computational efficiency.
Valencian Summer School 2015
Day 1
Lecture 3
Ensembles of Decision Trees
Gonzalo Martínez (UAM)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
Comparison Study of Decision Tree Ensembles for RegressionSeonho Park
Nowadays, decision tree ensemble methods are widely used for solving classification and regression problem due to their rigorousness and robustness. To compare with classification, the performance in regression problem so far has not been yet addressed in detail. In this presentation, we review the state-of-art decision tree ensemble methodology in scikit-learn and xgboost for regression. Also, empirical study results are illustrated to compare their performance and computational efficiency.
Valencian Summer School 2015
Day 1
Lecture 3
Ensembles of Decision Trees
Gonzalo Martínez (UAM)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
Machine Learning and Data Mining: 16 Classifiers EnsemblesPier Luca Lanzi
Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. In this lecture we introduce classifiers ensembles.
Dr. Trevor Hastie: Data Science of GBM (October 10, 2013: Presented With H2O)Sri Ambati
Dr. Trevor Hastie of Stanford University discusses the data science behind Gradient Boosted Regression and Classification
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Tree models with Scikit-Learn: Great models with little assumptionsGilles Louppe
This talk gives an introduction to tree-based methods, both from a theoretical and practical point of view. It covers decision trees, random forests and boosting estimators, along with concrete examples based on Scikit-Learn about how they work, when they work and why they work.
Overview of tree algorithms from decision tree to xgboostTakami Sato
For my understanding, I surveyed popular tree algorithms on Machine Learning and their evolution. This is the first time I wrote a presentation in English. So, I am happy if you give me a feedback.
Many firms have incorporated analytic solutions, but are struggling to apply reporting for advanced analysis. Pierre DeBois will review the various options in Google Analytics that will highlight influences on digital customer experience, such as identifying site or app traffic that reflect an intended consumer segment. The mini-workshop will also reveal how combining Google Analytics with an advanced tool, R programming, can highlight data trends—and thus customer experiences—that are potentially sustainable long term.
The session is about creating, training, evaluating and deploying machine learning with no-code approach using Azure AutoML.
* NO MACHINE LEARNING EXPERIENCE REQUIRED *
Agenda:
1. Introduction to Machine Learning
2. What is AutoML (Automated Machine Learning) ?
3. AutoML versus Conventional ML practices
4. Intro to Azure Automated Machine Learning
5. Hands-on demo
6 Contest
6. Learning resources
7. Conclusion
Meetup sthlm - introduction to Machine Learning with demo casesZenodia Charpy
Data science and Machine Learning
Machine Learning vs Artificial Intelligence
Machine Learning Algorithms
How to choose ML algorithm mindmap
Supervised Learning generic flow
Unsupervised Learning generic flow
Example cases for supervised and unsupervised learning
Ocado Technology is providing a full solution to put the world’s retailers online using the cloud, robotics, AI and IoT. Processing tens of thousands of orders every day, we generate millions of events every minute, leading to huge amount of data to be managed. We will present how this Big Data is handled in Google Cloud Platform to build a end-to-end machine learning pipeline: how data is stored and processed in BigQuery, post-processed and copied with Dataflow, then used to train Deep Neural Network models with TensorFlow, how all this is orchestrated using our in-house scheduling software called Query Manager, and how predictions are finally run in real-time using Cloud ML Engine and Datastore.
IMPLEMENTATION OF MACHINE LEARNING IN E-COMMERCE & BEYONDRabi Das
Presentation for the webinar held on 23rd May 2020, conducted by The IoT Academy for FDP program in collaboration with E&ICT Avademy, IIT Guwahati and delivered by Mr. Shree Kant Das, Growth and Digital Strategy Manager from noon.com.
Machine Learning and Data Mining: 16 Classifiers EnsemblesPier Luca Lanzi
Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. In this lecture we introduce classifiers ensembles.
Dr. Trevor Hastie: Data Science of GBM (October 10, 2013: Presented With H2O)Sri Ambati
Dr. Trevor Hastie of Stanford University discusses the data science behind Gradient Boosted Regression and Classification
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Tree models with Scikit-Learn: Great models with little assumptionsGilles Louppe
This talk gives an introduction to tree-based methods, both from a theoretical and practical point of view. It covers decision trees, random forests and boosting estimators, along with concrete examples based on Scikit-Learn about how they work, when they work and why they work.
Overview of tree algorithms from decision tree to xgboostTakami Sato
For my understanding, I surveyed popular tree algorithms on Machine Learning and their evolution. This is the first time I wrote a presentation in English. So, I am happy if you give me a feedback.
Many firms have incorporated analytic solutions, but are struggling to apply reporting for advanced analysis. Pierre DeBois will review the various options in Google Analytics that will highlight influences on digital customer experience, such as identifying site or app traffic that reflect an intended consumer segment. The mini-workshop will also reveal how combining Google Analytics with an advanced tool, R programming, can highlight data trends—and thus customer experiences—that are potentially sustainable long term.
The session is about creating, training, evaluating and deploying machine learning with no-code approach using Azure AutoML.
* NO MACHINE LEARNING EXPERIENCE REQUIRED *
Agenda:
1. Introduction to Machine Learning
2. What is AutoML (Automated Machine Learning) ?
3. AutoML versus Conventional ML practices
4. Intro to Azure Automated Machine Learning
5. Hands-on demo
6 Contest
6. Learning resources
7. Conclusion
Meetup sthlm - introduction to Machine Learning with demo casesZenodia Charpy
Data science and Machine Learning
Machine Learning vs Artificial Intelligence
Machine Learning Algorithms
How to choose ML algorithm mindmap
Supervised Learning generic flow
Unsupervised Learning generic flow
Example cases for supervised and unsupervised learning
Ocado Technology is providing a full solution to put the world’s retailers online using the cloud, robotics, AI and IoT. Processing tens of thousands of orders every day, we generate millions of events every minute, leading to huge amount of data to be managed. We will present how this Big Data is handled in Google Cloud Platform to build a end-to-end machine learning pipeline: how data is stored and processed in BigQuery, post-processed and copied with Dataflow, then used to train Deep Neural Network models with TensorFlow, how all this is orchestrated using our in-house scheduling software called Query Manager, and how predictions are finally run in real-time using Cloud ML Engine and Datastore.
IMPLEMENTATION OF MACHINE LEARNING IN E-COMMERCE & BEYONDRabi Das
Presentation for the webinar held on 23rd May 2020, conducted by The IoT Academy for FDP program in collaboration with E&ICT Avademy, IIT Guwahati and delivered by Mr. Shree Kant Das, Growth and Digital Strategy Manager from noon.com.
Which library should you choose for data-science? That's the question!Anastasia Bobyreva
This talk presents you the data-science ecosystem in two languages : Python and Scala. It demonstrates the use of their libraries on real dataset to solve binary classification problem with decision tree algorithm.
Business Process Analytics: From Insights to PredictionsMarlon Dumas
Keynote talk at the 13th Baltic Conference on Databases and Information Systems, Trakai, Lithuania, 2 July 2018.
Abstract
Business process analytics is a body of methods for analyzing data generated by the execution of business processes in order to extract insights about weaknesses and improvement opportunities, both at the tactical and operational levels. Tactical process analytics methods (also known as process mining) allow us to understand how a given business process is actually executed, if and how its execution deviates with respect to expected or normative pathways, and what factors contribute to poor process performance or undesirable outcomes. Meantime, operational process analytics methods allow us to monitor ongoing executions of a business process in order to predict future states and undesirable outcomes at runtime (predictive process monitoring). Existing methods in this space allow us to predict, for example, which task will be executed next in a case, when, and who will perform it? When will an ongoing case complete? What will its outcome be and how can negative outcomes be avoided? This keynote will present a framework for conceptualizing business process analytics methods and applications. The talk will provide an overview of state-of-art methods and tools in the field and will outline open challenges and research opportunities.
Big data, Machine learning and the AuditorBharath Rao
Check an insight as to how an Auditor can leverage Analytics, machine learning, and Technology to achieve absolute assurance and to effectively control the Fraud Risk present in the Enterprise.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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!
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
4. Difference from conventional approaches
1. Free from Limits of Conventional Approaches such as
Time Series Analysis,
Technical Analysis
and Fundamental Analysis
Machine Learning and Genetic Programming could be ;
2. Able to gain much smaller Standard Errors which mean that
our approarche is beyond Conventional Approaches
3. Possible that it can link with trading system through
research and development because of essentially computational
5. Variation of Trading System with Machine Learning
(source) Google Images
Conventional
approaches look like…
Solutions to hot issues in the markets;
>Automated Market Making
>Market Microstructure Trading
>Event Trading
>Statistical Arbitrage
