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Machine Learning

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  • Does it matter if an algorithm can't explain how it knows what it knows?
    Does it matter if an algorithm can't explain how it knows what it knows? Beau Walker, Founder, Method Data Science Recorded: May 24 2018 34 mins
    With the General Data Protection Regulation (GDPR) becoming enforceable in the EU on May 25, 2018, many data scientists are worried about the impact that this regulation and similar initiatives in other countries that give consumers a "right to explanation" of decisions made by algorithms will have on the field of predictive and prescriptive analytics.

    In this session, Beau will discuss the role of interpretable algorithms in data science as well as explore tools and methods for explaining high-performing algorithms.

    Beau Walker has a Juris Doctorate (law degree) and BS and MS Degrees in Biology and Ecology and Evolution. Beau has worked in many domains including academia, pharma, healthcare, life sciences, insurance, legal, financial services, marketing, and IoT.
  • Semantic AI: Bringing Machine Learning and Knowledge Graphs Together
    Semantic AI: Bringing Machine Learning and Knowledge Graphs Together Kirk Borne, Principal Data Scientist, Booz Allen Hamilton & Andreas Blumauer, CEO, Managing Partner Semantic Web Company Recorded: May 23 2018 64 mins
    Implementing AI applications based on machine learning is a significant topic for organizations embracing digital transformation. By 2020, 30% of CIOs will include AI in their top five investment priorities according to Gartner’s Top 10 Strategic Technology Trends for 2018: Intelligent Apps and Analytics. But to deliver on the AI promise, organizations need to generate good quality data to train the algorithms. Failure to do so will result in the following scenario: "When you automate a mess, you get an automated mess."

    This webinar covers:

    - An introduction to machine learning use cases and challenges provided by Kirk Borne, Principal Data Scientist at Booz Allen Hamilton and top data science and big data influencer.
    - How to achieve good data quality based on harmonized semantic metadata presented by Andreas Blumauer, CEO and co-founder of Semantic Web Company and a pioneer in the application of semantic web standards for enterprise data integration.
    - How to apply a combined approach when semantic knowledge models and machine learning build the basis of your cognitive computing. (See Attachment: The Knowledge Graph as the Default Data Model for Machine Learning)
    - Why a combination of machine and human computation approaches is required, not only from an ethical but also from a technical perspective.
  • How AI is Changing Marketing
    How AI is Changing Marketing Gil Allouche, CEO, Metadata.io Recorded: May 17 2018 43 mins
    In this webinar, Metadata.io CEO Gil Allouche will talk about the different ways AI is being used by marketers. From analyzing data to orchestrating new marketing campaigns, AI is powering marketing activities in new and exciting ways and affecting interactions throughout the entire customer lifecycle. As an example of how AI can have a tremendous impact on marketing practices, Gil will focus on its role in lead generation. Webinar attendees will learn:

    - What Machine Learning is in relation to AI and how it connects your data to find patterns
    - Examples of how machine learning can identify target audiences, including the 20 percent that creates 80 percent of your revenue
    - How AI technology can help marketers prioritize their budgets to focus on the most effective programs
    - Starting with small, iterative uses of AI in marketing can be the most effective way to understand what will yield the most ROI

    Gil Allouche founded Metadata.io to make demand generation easy for non-technical marketers. The Metadata.io platform and AI Operator evolved from Gil's experiences hacking various marketing and CRM systems to get the solutions he needed.
  • Customer-Centered AI: A Radical Strategy
    Customer-Centered AI: A Radical Strategy Geordie Kaytes, Partner, Heroic Recorded: May 16 2018 34 mins
    AI is a powerful tool, but often companies get more excited about their technology than in the customer value they’re creating. Geordie Kaytes will share a framework for building customer-centered AI products. You’ll learn how to craft a far-reaching vision and strategy centered around customer needs and balance that vision with the day-to-day needs of your company.

    Learn a framework for creating and communicating a vision that describes the overall direction of your AI product, a defined product strategy, a cross-functional roadmap aligned with the strategy, and a list of metrics that track progress towards the strategy

    About the Speaker: Geordie Kaytes is the director of UX strategy for Boston-area UI/UX studio Fresh Tilled Soil and a partner at Heroic (https://www.heroicteam.com), a design leadership coaching firm that helps growing companies scale their digital product capabilities. A digital product design leader with deep experience in design process transformation and cross-functional expertise in design, strategy, and technology, Geordie has helped companies in a broad range of industries develop a 360-degree view of their product design processes. Previously, he did his obligatory tour of duty in management consulting. He holds a BA from Yale in political science. He is a coauthor of the Medium publication Radical Product.
  • Intelligent Agents and a New Class of Perceived Errors
    Intelligent Agents and a New Class of Perceived Errors Dennis R. Mortensen, CEO and Founder, x.ai Recorded: May 16 2018 47 mins
    As we move to the conversational UI and take advantage of NLP and AI in general, we change the way we interact with technology dramatically. The standard GUI is many times fully eliminated, leading to novel challenges in UX. Tasks are removed from the user’s oversight with invisible or seamless software, and the output is not always as expected. But sometimes that output is correct within the parameters given and simply perceived as an error.

    Dennis will talk through where x.ai has encountered error perception issues as we seek to develop frictionless software, how we thought about the problem and the communication strategies we’re exploring to resolve it.
  • Network Telemetry & Analytics in the Age of Big Data & AI
    Network Telemetry & Analytics in the Age of Big Data & AI Ruturaj Pathak, Senior Product Manager, Networking BU, Inventec Recorded: May 15 2018 35 mins
    We are seeing a sea change in networking. SDN has enabled improvements in network telemetry and analytics.

    In this presentation, I will talk about the current challenges that are out there and how the technology change is helping us to improve the overall network telemetry. Furthermore, I will share how deep learning techniques are being used in this field. Please join this webinar to understand how the field of network telemetry is changing.
  • The Predictive Bank of the Future: How AI will Change Banking Forever
    The Predictive Bank of the Future: How AI will Change Banking Forever Tariq Ali Asghar, CEO, Emerging Star investment Group Recorded: May 15 2018 47 mins
    This Webinar explains how Big Data, Artificial Intelligence, and Machine Learning is going to transform the future Banking Industry. Banks which can manage this Big Data evolution successfully will survive and thrive, and give a more holistic and personalized customer service, thereby increasing their revenues tremendously.

    The key takeaway from this Webinar is that “Right information at the right place and the right time is going to be the real money and will shape the future of Banking Industry.”

    Tariq is a Fintech Expert, writer, and thinker based in Toronto Canada and is currently working on an initiative to disrupt the conventional Banking Industry with “Big Data Predictive Analytics Model” of his startup.
  • An Introduction to Deep Learning
    An Introduction to Deep Learning Mustafa Kabul, Principal Data Scientist, SAS Recorded: May 15 2018 64 mins
    In this webinar, Mustafa Kabul, Principal Data Scientist, SAS, will provide an introduction to deep learning and its applications.

    Mustafa is a data scientist in the Artificial Intelligence and Machine Learning R&D at SAS, where he leads innovative projects for SAS’s next-generation AI-enabled analytics products, including applications of deep learning. His current focus is on applying deep reinforcement learning to operational problems in the CRM and IoT spaces. An operations research expert working at the interface of machine learning and optimization, previously, he developed distributed, large-scale integer optimization algorithms for marketing optimization problems. Ever the optimization enthusiast, Mustafa always looks into ways to improve the algorithms. Nowadays his favorites are the distributed stochastic gradient and online learning methods. Mustafa holds a PhD from the University of North Carolina at Chapel Hill, where his research focused on game theory models of supply chains selling to strategic customers.
  • Implementing a Sparse Logistic Regression Algorithm in Apache Spark
    Implementing a Sparse Logistic Regression Algorithm in Apache Spark Lorand Dali, Data Scientist, Zalando Recorded: Mar 29 2018 39 mins
    This talk tells the story of implementation and optimization of a sparse logistic regression algorithm in spark. I would like to share the lessons I learned and the steps I had to take to improve the speed of execution and convergence of my initial naive implementation. The message isn’t to convince the audience that logistic regression is great and my implementation is awesome, rather it will give details about how it works under the hood, and general tips for implementing an iterative parallel machine learning algorithm in spark.

    The talk is structured as a sequence of “lessons learned” that are shown in form of code examples building on the initial naive implementation. The performance impact of each “lesson” on execution time and speed of convergence is measured on benchmark datasets.

    You will see how to formulate logistic regression in a parallel setting, how to avoid data shuffles, when to use a custom partitioner, how to use the ‘aggregate’ and ‘treeAggregate’ functions, how momentum can accelerate the convergence of gradient descent, and much more. I will assume basic understanding of machine learning and some prior knowledge of spark. The code examples are written in scala, and the code will be made available for each step in the walkthrough.

    Lorand is a data scientist working on risk management and fraud prevention for the payment processing system of Zalando, the leading fashion platform in Europe. Previously, Lorand has developed highly scalable low-latency machine learning algorithms for real-time bidding in online advertising.
  • Having fun with Raspberry(s) and Apache Projects
    Having fun with Raspberry(s) and Apache Projects Jean-Frederic Clere, Manager, Software Engineering, Red Hat Recorded: Mar 29 2018 49 mins
    You can do a lot with a Raspberry and ASF projects. From a tiny object
    connected to the internet to a small server application. The presentation
    will explain and demo the following:

    - Raspberry as small server and captive portal using httpd/tomcat.
    - Raspberry as a IoT Sensor collecting data and sending it to ActiveMQ.
    - Raspberry as a Modbus supervisor controlling an Industruino
    (Industrial Arduino) and connected to ActiveMQ.

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