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Machine Learning

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  • Radiant, a powerful open source Shiny application for business analytics
    Radiant, a powerful open source Shiny application for business analytics Ali Marami Chief Data Scientist Recorded: Jul 11 2017 58 mins
    Radiant is a robust tool for business analytics and running sophisticated models without any need for code development. It leverages the functions and tools in R and at the same time provides a user-friendly interface. With Radiant, you can manipulate and visualize your data, run different models from simple OLS to decision trees (CART) and neural networks, and evaluate your results.

    The application is based on the Shiny package and can be run locally or on a server. Radiant was developed by Vicent Nijs. In this webinar, we review the tools available in Radiant and explain how easily you can use this tool without any setup or installation on your system.

    Radiant key features:

    • Explore: Quickly and easily summarize, visualize, and analyze your data
    • Run different models: OLS, GLM, Neural Networks, Naïve Bayes and CART.
    • Cross-platform: It runs in a browser on Windows, Mac, and Linux
    • Reproducible: Recreate results and share work with others as a state-file or an Rmarkdown report
    • Programming: Integrate Radiant's analysis functions with your own R-code
    • Context: Data and examples focus on business applications

    After this webinar you will learn:

    • Data manipulation and running different models
    • How to run advanced analytics in a browser on any device even in your tablet or iPad.


    Presenter bio:

    Ali has a Ph.D. in Finance from the University of Neuchatel in Switzerland and a BS in Electrical Engineering. He has extensive experience in financial modeling, quantitative modeling, and financial risk management in several US banks.
  • Binomial and Multinomial Logistic Regressions in R
    Binomial and Multinomial Logistic Regressions in R Ali Marami Chief Data Scientist Recorded: Jun 29 2017 49 mins
    Logistic regressions are the basic of machine learning. In this webinar, we discuss binomial and multinomial logistic regressions, how we implement them in R and test their performance. We will also review few examples of their usage in industry. In addition, you will learn how to use R-Brain advanced IDE when implementing the model.

    - Logistic regressions fundamentals and how to interpret estimates
    - Binomial and Multinomial logistic regressions
    - Implement logistic regressions in R
    - Performance measurement in logistic regressions
    - Generating and understanding ROC curve
    - Building confusion metrics and understanding its elements
    - Examples of model application in industry
    - Learn about new advanced IDE

    Presenter bio:

    Ali has a Ph.D. in Finance from the University of Neuchatel in Switzerland and a BS in Electrical Engineering. He has extensive experience in financial modeling, quantitative modeling, and financial risk management in several US banks.
  • Death to Traffic: How Smart Cities are Changing Transportation
    Death to Traffic: How Smart Cities are Changing Transportation Laura Schewel, CEO, StreetLight Data Recorded: Jun 23 2017 45 mins
    From automated vehicles to ride hailing apps, transportation as we know it is changing - and fast. But new technologies alone won't help communities build the efficient, equitable, and sustainable transportation networks communities want. In fact, these innovative technologies could do just the opposite, especially if they are not deployed wisely. Cities must collect the right data and enact the right policies to ensure they do not exacerbate problems like inequity and traffic, and to hold themselves accountable to the promise of new mobility technologies.

    In this webinar, you will find out why - and how - the smartest cities of tomorrow will be those that adopt data-driven transportation strategies today. Join for Laura Schewel's presentation to gain insights into:

    • Why the status quo for transportation data collection is no longer good enough
    • The types of Massive Mobile Data that are useful for transportation and urban planning
    • Algorithmic processing techniques that are critical for making this data useful
    • Case studies from California and Virginia that demonstrate why Massive Mobile Data drives more effective transportation planning
    • A forward-looking blueprint for using Massive Mobile Data to maximize the potential benefits of new transportation technologies - and minimize negative impacts

    Laura Schewel founded StreetLight Data, a mobility analytics provider, after spending more than a decade as an advanced transportation researcher and statistician at the Rocky Mountain Institute and FERC. She has particular expertise in transportation systems, sustainability and safety, and vehicle/system modeling and analysis.
  • How IoT Will Make Healthcare Healthy, Wealthy, & Wise
    How IoT Will Make Healthcare Healthy, Wealthy, & Wise Jarie Bolander, COO, Lab Sensor Solutions Recorded: Jun 23 2017 46 mins
    IoT is a technology that has the potential to make us healthy, wealthy, and wise especially in healthcare. Healthcare is just now adopting IoT to improve patient outcomes and decrease the cost of care.

    In this webinar, you’ll learn:

    - How to identify if an IoT solution will work for your use case.
    - What others in healthcare are using IoT for.
    - The challenges of IoT in healthcare
  • Panel: Smart Fog and Transaction Management for Cities and Maritime
    Panel: Smart Fog and Transaction Management for Cities and Maritime Moderator: Katalin Walcott (Intel) Panel: Jeff Fedders (OpenFog), Mark Dixon (IBM), Matthew Bailey (Powering IoT) Recorded: Jun 22 2017 61 mins
    Fog computing represents a tectonic shift for the future of transaction management, distributed supply chain and overall experience. It blurs the lines between the edge and the cloud and puts the focus on the systems which manage and balance the delivery of coherent, end-to-end sessions and associated transaction level agreements. As a result, this new technology is pervasive in several industries.

    Join this panel of experts as they discuss solutions with specific industry use cases from smart fog for Cities, Buildings, Ports, and Maritime.

    Moderator: Katalin Walcott, Work Group Chair Manageability at OpenFog Consortium & Principal Engineer - IoT/Fog Computing Orchestration Architecture at Intel

    Panelists:
    - Jeff Fedders, President at OpenFog Consortium & Chief Strategist, IoTG Strategy and Technology Office at Intel
    - Mark Dixon, Senior Architect for Smarter Cities at IBM
    - Matthew Bailey, President, Powering IoT - Smart City advisor and strategist to governments, technology corporations, and economic development agencies
  • Toward Internet of Everything: Architectures, Standards, & Interoperability
    Toward Internet of Everything: Architectures, Standards, & Interoperability Ram D. Sriram, Chief of the Software and Systems Division, IT Lab at National Institute of Standards and Technology Recorded: Jun 21 2017 63 mins
    In this talk, Ram will provide a unified framework for Internet of Things, Cyber-Physical Systems, and Smart Networked Systems and Societies, and then discuss the role of ontologies for interoperability.

    The Internet, which has spanned several networks in a wide variety of domains, is having a significant impact on every aspect of our lives. These networks are currently being extended to have significant sensing capabilities, with the evolution of the Internet of Things (IoT). With additional control, we are entering the era of Cyber-physical Systems (CPS). In the near future, the networks will go beyond physically linked computers to include multimodal-information from biological, cognitive, semantic, and social networks.

    This paradigm shift will involve symbiotic networks of people (social networks), smart devices, and smartphones or mobile personal computing and communication devices that will form smart net-centric systems and societies (SNSS) or Internet of Everything. These devices – and the network -- will be constantly sensing, monitoring, interpreting, and controlling the environment.

    A key technical challenge for realizing SNSS/IoE is that the network consists of things (both devices & humans) which are heterogeneous, yet need to be interoperable. In other words, devices and people need to interoperate in a seamless manner. This requires the development of standard terminologies (or ontologies) which capture the meaning and relations of objects and events. Creating and testing such terminologies will aid in effective recognition and reaction in a network-centric situation awareness environment.

    Before joining the Software and Systems Division (his current position), Ram was the leader of the Design and Process group in the Manufacturing Systems Integration Division, Manufacturing Engineering Lab, where he conducted research on standards for interoperability of computer-aided design systems.
  • The Secrets to WINNING with Machine Learning
    The Secrets to WINNING with Machine Learning Patrick Rice, CEO, Lumidatum Recorded: Apr 13 2017 39 mins
    It’s easy to get caught in the excitement of machine learning and start optimizing RMSE, AUC or recall, but machine learning success starts with aligning to the business.

    Join Patrick Rice, CEO of Lumidatum, as he leverages his decade of experience in big data analytics including implementing machine learning solutions at Amazon to outline the secrets to winning at machine learning and delivering real ROI to the business.
  • Machine Learning To Increase Human Understanding
    Machine Learning To Increase Human Understanding Assaf Baciu, Co-founder, SVP of Product & Engineering at Persado Recorded: Apr 13 2017 46 mins
    There is a misconception that infusing automation into the customer experience will make it less human, and thus less relatable, to consumers. On the contrary, companies are pioneering AI technologies to enable them to gain a deeper understanding of the people they serve, resulting in experiences that are more relevant, personal, emotional, and ultimately rewarding--for customer and company alike.

    Join Assaf Baciu, head of product and co-founder of Persado, who will demonstrate how Fortune 1000 brands are leveraging machine learning to create emotional relationships with their audiences.
  • Semantic Natural Language Understanding w/ Spark, ML Annotators & DL Ontologies
    Semantic Natural Language Understanding w/ Spark, ML Annotators & DL Ontologies David Talby, CTO, Atigeo Recorded: Apr 12 2017 62 mins
    A text-mining system must go way beyond indexing and search to appear truly intelligent. First, it should understand language beyond keyword matching. (e.g. distinguishing between “Jane has the flu,” “Jane may have the flu,” “Jane is concerned about the flu," “Jane’s sister has the flu, but she doesn’t,” or “Jane had the flu when she was 9” is of critical importance.) This is a natural language processing problem. Second, it should “read between the lines” and make likely inferences even if they’re not explicitly written. (e.g. if Jane has had a fever, a headache, fatigue, and a runny nose for three days, not as part of an ongoing condition, then she likely has the flu.) This is a semisupervised ML problem. Third, it should automatically learn the right contextual inferences to make. (e.g. learning on its own that fatigue is sometimes a flu symptom—only because it appears in many diagnosed patients—without a human ever explicitly stating that rule.) This is an association-mining problem, which can be tackled via deep learning or via more guided ML techniques.

    David Talby leads a live demo of an end-to-end system that makes nontrivial clinical inferences from free-text patient records and provides real-time inferencing at scale. The architecture is built out of open source big data components: Kafka and Spark Streaming for real-time data ingestion and processing, Spark for modeling, and Elasticsearch for enabling low-latency access to results. The data science components include spaCy, a pipeline with custom annotators, machine-learning models for implicit inferences, and dynamic ontologies for representing and learning new relationships between concepts.

    David Talby is Atigeo’s CTO, working to evolve its big data analytics platform to solve real-world problems in healthcare, energy, and cybersecurity. David has extensive experience in building & operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams.
  • Panel: AI - The Backbone of the Modern Startup
    Panel: AI - The Backbone of the Modern Startup Panel: Mike Schmidt (Dovetale); Brett Kuprel (Stanford AI Lab); Vijay Nadadur (Stride.ai); Joshua Montgomery (Mycroft AI) Recorded: Apr 11 2017 54 mins
    Panel discussion on the advent of artificial intelligence and machine learning in startups and business.

    Panelists:
    Mike Schmidt, Co-Founder, Dovetale, Inc.
    Vijay Nadadur, Co-Founder & CEO, Stride.ai, Inc.
    Joshua Montgomery, CEO, Mycroft AI
    Brett Kuprel, Ph.D. Student, Standford AI Lab

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