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    • How the Machine Learning Wave is Changing the Way Companies Look at Analytics How the Machine Learning Wave is Changing the Way Companies Look at Analytics Andrew Pease, Business Solutions SAS; Patrick Hall, Machine Learning Scientist SAS; Ben Lorica, Data Scientist O'Reilly Media Upcoming: Jun 9 2016 3:00 pm UTC 60 mins
    • Machine learning is changing the way organizations look at analytics. Data scientists are being recognized as a key component in organizational analytics, but management often doesn't understand their work or know how to effectively manage them.

      Many businesses understand that analytics has moved beyond the data warehouse, and are pushing analysts and IT to grab and analyze data from new sources, even though they may not be ready to derive business value from it.

      Open source is seen as the path to machine learning innovation, despite challenges with deployment and approachable user interfaces. For organizations using or looking to adopt machine learning techniques, moving forward may be a challenge and measuring success even trickier.

      In this webcast, we will:

      -Discuss how different organizations are finding success with machine learning.

      -Look at how organizations are feeding the creativity of data scientists, making analytics accessible to business experts, and pushing the analytics closer to the data.

      -Identify how organizations are automating analytics processes in order to free up time for new analytics, new data and new business problem domains, ultimately creating real competitive advantage.

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    • Experiments in Deep Learning Experiments in Deep Learning Patrick Hall, Senior Associate Research Statistician Developer, SAS Recorded: May 28 2015 6:00 pm UTC 35 mins
    • The human brain makes it look easy. What our eyes see, we decode immediately and effortlessly. But is it that simple? In truth, how we process images is staggeringly complex. Inspired in part by our remarkable neurons, deep learning is a fast-growing area in machine learning research that shows promising breakthroughs in speech, text and image recognition. It’s based on endowing a neural network with many hidden layers, enabling a computer to learn tasks, organize information and find patterns on its own.

      Recently, SAS took on a classical problem in machine learning research, the MNIST database, a data set containing thousands of handwritten digit images. Learn how we did – and what it reveals about the future of deep learning.

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    • Introduction to Machine Learning Introduction to Machine Learning Wayne Thompson, Chief Data Scientist, SAS Recorded: May 19 2016 2:25 pm UTC 39 mins
    • Machine learning continues to be a hot topic for organizations looking to get more accuracy from their analytic models. But there are several practical issues that should be considered in applying it to real-world industry problems.

      In this this webinar, Wayne Thompson of SAS delves into those issues and provides an overview of machine learning, as well as key business applications of this technique, including fraud detection, model factories and recommendation systems.

      Key Takeaways

      -Learn about four focus areas of machine learning: unsupervised learning, supervised learning, semisupervised learning and reinforcement learning.

      -Get useful tips on feature engineering, ensemble modeling, bias variance, shrinkage and model evaluation.

      -Get insight into the future of machine learning, including cognitive computing and robot learning.

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    • Self-Learning Search for Salesforce Communities Self-Learning Search for Salesforce Communities Coveo Recorded: May 4 2016 4:00 pm UTC 45 mins
    • Watch this webinar replay to learn how Coveo for Salesforce brings intelligent search, powered by machine-learning, to Salesforce Communities. Using live demos and real-world success stories, we discuss how organizations can turn their community into a true self-service engine. You'll learn how to:

      - Boost case deflection and prevention
      - Minimize calls to the contact center
      - Relieve administrators from tedious manual tuning tasks
      - Make customers happier and more loyal

      For any organization committed to delivering high-impact self-service on their Salesforce Community, this info-packed session is a must see.

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    • How Big Data and Machine Learning are Transforming ITSM How Big Data and Machine Learning are Transforming ITSM Lawrence Hall, Distinguished Professor at the University of SF & John Prestridge, VP of Marketing & Product Strategy Recorded: May 26 2016 4:00 pm UTC 53 mins
    • As we move into a new era of ITSM computing, new big data and machine learning tools and methodologies are being developed to support IT staff by intelligently extracting insights and making predictions from the enormous amounts of data accumulated from the organization. According to Gartner, I&O leaders must take a comprehensive approach to incorporate advanced big data and machine learning technologies into their organizations or risk becoming irrelevant. But what exactly is big data and machine learning all about? How can you introduce these concepts into your existing Service Desk?

      Join USF’s distinguished Computer Science and Engineering Professor Lawrence Hall and SunView Software’s VP of Marketing and Product Strategy John Prestridge as they break down the fundamentals of big data and machine learning and provide real-world examples of the impact the technologies will have on ITSM.

      About Lawrence:

      Lawrence Hall is a Distinguished University Professor of Computer Science and Engineering at University of South Florida. He has authored over 190 publications in journals, conferences, and books. Recent publications appear in Pattern Recognition, IEEE Access, IEEE Transactions on Fuzzy Systems, and the International Conference on Pattern Recognition.

      Lawrence has received funding from the National Institutes of Health, NASA, DOE, National Science Foundation and others. His research interests lie in distributed machine learning, extreme data mining, bioinformatics, pattern recognition and integrating AI into image processing.

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