Sep 9–12, 2019

AI for managers (Day 2)

Location: 111

Who is this presentation for?

  • You're a business professional who wants to learn about big data.
  • You work with data scientists or analysts regularly.
  • You manage teams or projects with a significant data component.
  • You find yourself translating between data and management.

Level

Beginner

Description

Outline

Introduction to AI and data science

  • Terms and definitions: What does machine learning mean?
  • Historical context and present day
  • Drivers for AI and data science
  • What’s so different about big data?
  • AI is eating the world
  • Making AI practical

Algorithms and techniques

  • Data formats, databases, and schemas
  • Evaluating model performance and validating models
  • Terminology: Regression, classification, supervised, and unsupervised
  • Advanced models: Random forests, support vector machines, deep learning, and neural networks

Industry use cases

  • Finance
  • Healthcare
  • Industrial
  • Technology

AI within the organization

  • Maturity levels for AI
  • Evaluating good projects for AI
  • Build versus buy and hire versus train
  • Skills, tools, and platforms needed for AI
  • Structuring data and AI initiatives within your organization: Successful and cautionary tales

Common pitfalls and fallacies in AI and data science

  • AI and data science in the headlines: The good, the bad, and the ugly
  • Legal and regulatory implications
  • Litigation and liabilities of bad data science
  • Common fallacies in data science and AI
  • Lying with statistics and how to spot it

What you'll learn

  • Identify and prioritize which projects your company should pursue and identify potential pitfalls in your projects before you start
  • Learn to communicate business objectives to data professionals and translate data science insights for business professionals and decision makers
  • Understand the business implications of technical decisions
  • Be able to assess the risk-reward trade-offs of different projects
  • Intel AI
  • O'Reilly
  • Amazon Web Services
  • IBM Watson
  • Dataiku
  • Dell Technologies
  • Intuit
  • Gamalon
  • H2O.ai
  • Hewlett Packard Enterprise
  • MapR Technologies
  • Sisu Data
  • Intuit
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