Ce diaporama a bien été signalé.
Nous utilisons votre profil LinkedIn et vos données d’activité pour vous proposer des publicités personnalisées et pertinentes. Vous pouvez changer vos préférences de publicités à tout moment.

P 03 ml_demystified_2017_05_02_v7

374 vues

Publié le

ML, AI, DL, DS, AA - are all of these the same or are they different? So much confusion. Learn how ML can be applied (with Insurance as an case study).

Publié dans : Données & analyses
  • Identifiez-vous pour voir les commentaires

  • Soyez le premier à aimer ceci

P 03 ml_demystified_2017_05_02_v7

  1. 1. Machine Learning Demystified Vishwa Kolla Head of Advanced Analytics John Hancock Insurance
  2. 2. Technology (CS) Analytics (Math, Stats) Business (MBA)  Advanced Analytics CoE, Maturity Model  Customer Analytics (entire value chain)  Machine Learning  Scoring Engine  Optimization  Simulations  Forecasting & Time Series • 16+ Years • John Hancock Insurance • Deloitte Consulting (Industries – Insurance, Retail, Financial, Technology, Telecom, Healthcare, Data) • IBM • Sun Microsystems Expertise Experience Vishwa Kolla Head of Advanced Analytics John Hancock Insurance, Boston MBA Carnegie Mellon University MS University of Denver BS BITS Pilani, India
  3. 3. What? Why? How?
  4. 4. AI, ML, DL, Data Science, Advanced Analytics…  Are all the same  Are very different  Not sure
  5. 5. AI, Data Science, ML, DL …  Are all the same  Are very different  Not sure Are related, but different
  6. 6. In terms of evolution, what is the right order? A. ML -> DL -> AI B. AI -> DL -> ML C. AI -> ML -> DL D. DL -> ML -> AI
  7. 7. In terms of evolution, what is the right order? A. ML -> DL -> AI B. AI -> DL -> ML C. AI -> ML -> DL D. DL -> ML -> AI
  8. 8. How are all of these related? Source: h2o.ai Computer Science (CS) The study of automating algorithmic processes that scale Artificial Intelligence (AI) An ideal intelligent machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at an arbitrary goal Machine Learning (ML) The study and construction of algorithms that can learn from and make predictions on data Deep Learning (DL) A branch of machine learning based on a set of algorithms that model high-level abstractions in data using multiple processing layers
  9. 9. Evolution https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/
  10. 10. Data Science Analytics (Math) Technology (CS) Business (BBA/MBA)
  11. 11. Advanced Analytics Business Data Math Implement Internal External Merge Profile Segment Explore Campaign Execution Nudge Videos Ops Integration Apps Applications BI Strategy Insights Recos Monitor Geo-Spatial
  12. 12. What? Why? How?
  13. 13. Gartner Hype Cycle 2015
  14. 14. Gartner Hype Cycle 2016
  15. 15. The promise is real
  16. 16. Value from Internal Data is … well, HUGE ~$820 B Value of Customer Data + Algorithms $1.2 T Market Cap (11/30/2016) $120 B Debt (11/30/2016) $178 B Brand Value (05/2016) Source: http://www.forbes.com/powerful-brands/list/2/#tab:rank
  17. 17. Embracing Data helps with Top & Bottom lines 2001 – 2013 CAGR Revenue (Firm | Industry) Source: 2001 – 2013 Revenue figures from Capital IQ 3% 3% 3% 1% 5% 7% 7% 8% 10% 12%
  18. 18. What? Why? How?
  19. 19. Machine Learning (ML) gives computers (machine) ability to learn (learning) without being explicitly programmed (learning) Arthur Samuel, 1959
  20. 20. ML techniques Machine Learning Supervised Learning Classification SVM Discriminant Analysis Naïve Bayes Nearest Neighbor Regression Linear, GLM Trees (RF, GBM) Ensemble Neural Networks Un- supervised Learning Clustering K-Means / K-Medioids Hierarchical Neural Networks
  21. 21. Un-supervised Learning Techniques Source: Machine Learning eBook by Matlab K-Means K-Medoids Hierarchical SOMs
  22. 22. Un-supervised learning Applications
  23. 23. Supervised Learning Source: Machine Learning eBook by Matlab
  24. 24. Prospect Acquire Nurture
  25. 25. ML Use Cases in Life Insurance Prospecting NurtureAcquisition Market Segments Customer Segments Likely To [*] Media Mix Channel Strategy Survey Analytics Cross-Sell OCR Engines Mortality Risk Morbidity Risk Stratified Models Loss Ratio APS Summary Smoker Likelihood Churn Models Audience Propen- sities Claim Severity Customer Journey Litigation Likelihood Customer Engagem ent Fraud Detection >> Text Analytics Optimi- zation Simu- lations Recruiting Analytics IoT Analytics TV Audience Analytics Anomaly Detection >>
  26. 26. New Use Cases in Insurance – Age, BMI
  27. 27. New Use Cases in Insurance – Age, BMI
  28. 28. DL – the next frontier Vision Context Transcription Translation
  29. 29. 29
  30. 30. Sources and Acknowledgements 1. Gartner Hype Cycle. http://www.gartner.com/newsroom/id/3412017 2. Deep Learning at Google. https://www.wired.com/2016/02/ai-is-changing-the-technology-behind-google-searches/ 3. WSJ. Economic Value of AI. https://blogs.wsj.com/cio/2017/04/28/lower-prediction-costs-the-simple-economic-value-of-artificial-intelligence/ 4. John McCarthy. Father of AI. http://www.asiapacific-mathnews.com/04/0403/0015_0020.pdf