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Gary Hope - Machine Learning: It's Not as Hard as you Think
The document discusses the advancements and applications of machine learning, highlighting its impact on decision-making processes in businesses and everyday life. It emphasizes how companies like Microsoft have integrated machine learning into their products to enhance efficiency and predictive capabilities. Additionally, it outlines the workflow and best practices for implementing machine learning solutions across various fields.
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Gary Hope - Machine Learning: It's Not as Hard as you Think
- 1. THE WORLD WELIVE IN Speaker 4 of 17 Gary Hope @GaryHope Machine Learning – It’s Not as Hard as You Think Followed by Gillian Staniland
- 2. What is MachineLearning? Data and Decisions Data Science Workflow for Machine Learning Data Science Workflow for Machine Learning
- 3. What is MachineLearning? Delivering on one of the old dreams of Microsoft co-founder Bill Gates: Computers that can see, hear and understand. John Platt Distinguished scientist at Microsoft Research A breakthrough in machine learning would be worth ten Microsofts. Bill Gates Predictive computing systems that become smarter with experience “ “ ” ”
- 4. Me, Microsoft &Machine Learning 15 years of realizing innovation 1999 2004 2005 2008 2010 2012 2014 SQL Server enables data mining Computers work on users behalf, filtering junk email Microsoft Kinect can watch users gestures Microsoft launches Azure Machine Learning Microsoft search engine built with machine learning Bing Maps ships with ML traffic-prediction service Successful, real-time, speech-to-speech translation John Platt, Distinguished scientist at Microsoft Research Machine learning is pervasive throughout “ Microsoft products. ”
- 5. Why the resurgencein predictive analytics?
- 8. When presented withinformation we tell ourselves stories, we have biases and we have a very low level of intuitive understanding of statistical information (that’s not to say we cant spend the effort to analyze)
- 9. Any sufficiently advancedtechnology is indistinguishable from magic.. Arthur C. Clarke, 1961 If and to what extent the magic of Machine Learning changes YOUR world depends on how YOU use it! “ ” If you not actually using the data available to make systematic decisions in your business you will mostly be guessing or at best relying heavily on potentially biased intuition
- 10. The United StatesPostal Service processed over 150 billion pieces of mail in 2013—far too much for efficient human sorting. But as recently as 1997, only 10% of hand-addressed mail was successfully sorted automatically.
- 11. The challenge inautomation is enabling computers to interpret endless variation in handwriting.
- 12. By providing feedback,the Postal Service was able to train computers to accurately read human handwriting. Today, with the help of machine learning, over 98% of all mail is successfully processed by machines.
- 13. How Does Machine Learning Work? 1 1 5 4 3 7 5 3 5 3 5 5 9 0 6 3 5 2 0 0
- 14. Smart Buildings: IoTand ML example The Center for Building Performance and Diagnostics uses weather forecasts, real-time temperature reads, and behavioral research data to optimize building heating and cooling systems in real-time. Key Benefits • User friendly set up and integration with The ease of implementation makes machine learning accessible to a larger number of investigators with various backgrounds—even non-data scientists. Bertrand Lasternas Carnegie Mellon existing systems • Seamless data handling • Accessible and easy to use across backgrounds • Quickly compare algorithms “ ”
- 15. Using past datato predict the future Imagine what machine learning could do for your business. Churn analysis Equipment monitoring Spam filtering Ad targeting Recommendation Fraud detection Image detection & classification Forecasting Anomaly detection
- 16. Common Classes ofProblems Classification Regression Recommenders Anomaly Detection
- 17. Some quick theory:Linear Models
- 18. Machine Learning ProblemRequirements Available data • Related to the decision • Historical • Outcomes Valuable business problem involving a decision – Existing process – Metrics
- 19. Universal Machine LearningFlow • Define Objective • Measurable and has supporting data • Collect & Prepare Data Define Objective • Flatten schema, • normalize and common scale • Feature selection • Sample and split • Train Model • Algorithm selection • Parameter Sweeping • Analyze Results • Score, evaluate and visualize Collect & Prepare Data Train Model Analyze Results
- 27. Put ML intoProduction Technically make available as a published service Share usage and outcome information inside of the organization. Define Prepare Train Analyze Publish Use Monitor