1 / 16

algorithms ● intelligence ● optimisation

algorithms ● intelligence ● optimisation. Daniel Hulme ● daniel@satalia.com @ TheSolveEngine ● 07773765097 Masters ( Msci ) in Computer Science with Machine Learning @ UCL Doctorate ( EngD ) in Computational Complexity @ UCL Research Scientist in Optimisation & Innovation @ UCL

nam
Télécharger la présentation

algorithms ● intelligence ● optimisation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. algorithms ● intelligence ● optimisation Daniel Hulme ● daniel@satalia.com @TheSolveEngine● 07773765097 Masters (Msci) in Computer Science with Machine Learning @ UCL Doctorate (EngD) in Computational Complexity @ UCL Research Scientist in Optimisation & Innovation @ UCL Impact Associate for Dept. of Computer Science @ UCL Lecturer New Venture Analytics // Software Engineering @ UCLEntrepreneurship // Decision Making // Communications @ Pearson Founder & CEO of Satalia (NPComplete Ltd) @ UCL Recipient of a Kauffman Global Scholarship Visiting Fellow in BigData @ The Big Innovation Centre Board Director and Advisor to various UK and US companies

  2. Beyond BigData • ACTION • BIG WISDOM • BIG UNDERSTANDING • INSIGHT • BIG KNOWLEDGE • BIG INFORMATION • DATA • BIG DATA DIKUW Pyramid

  3. What is BigData?

  4. Giving Meaning to Data Structured • Databases • Siloed • Migrating from • Storage • Corruption • Security • Mining Unstructured • Internet • Trawling • Mining • Language • Privacy • Cleaning • Authenticity Semi-structured • Semantic Web • Tagging • Querying • Provenance • Mining • Storage • Migrating to

  5. Pretty Pictures & Data Scientists

  6. Why? Machine Learning • Mature subject • Complex Correlations • Open-source Tools • Mining • Prediction • Hard Semantic Inference • Reasoning • New research area • Semantic Web • Emerging Tools

  7. The Use of Knowledge BigQuestions • What problem are you trying to solve? • Objectives, Variables and Constraints Wisdom- Buy John a dog bowl for his birthday and he'll be very happy Understanding - John's birthday is on April 27th. If John Smith likes Dogs then he probably has one Knowledge - 1979-04-27 is John Smith's date of birth, and John Smith likes Dogs Information- 1979-04-27 is a Date, John Smith is a Person, Dog is an Animal (data in context) Data- "19790427", "John Smith", "Dog" (raw groups of symbols) Odd or Even: O(1) Ordered Search: O(log n) Sorting Items: O(n2) Travelling Salesman: O(n!)

  8. The Big “O” Odd or Even: O(1) Ordered Search: O(log n) Sorting Items: O(n2) Travelling Salesman: O(n!)

  9. Optimisation-as-a-Service

  10. Innovation Model Next Generation IP Disruptive Innovations Exchange Community Industry Data and Rewards Royalty Mechanism

  11. Knowledge, Power, Responsibility

  12. Questions & Discussions

  13. sataliaalgorithms ● intelligence ● optimisation Daniel Hulme ● daniel@satalia.com @TheSolveEngine ● 07773765097

  14. Web-portal and Services • Algorithm Scoping & Optimisation Insights • Modelling & Algorithm Design • Seamless Integration& Future-Proofing

More Related