Artificial Intelligence for Managers Training

Course #:WA2713

Artificial Intelligence for Managers Training

Artificial Intelligence is everywhere and today's business managers need to understand and embrace it.  AI can help businesses reengineer their processes for higher revenue, higher customer satisfaction and lower cost. This course teaches the fundamentals of artificial intelligence and how machine learning is a crucial component of the AI infrastructure.  Participants will learn how machine learning differs from traditional rule based software and explore via examples and live demonstration how AI and ML can be applied to business applications.

Audience

  • Product managers
  • Project managers
  • IT managers
  • Business executives

Duration

1 days

Outline of Artificial Intelligence for Managers Training

Chapter 1. Introduction to AI

  • Artificial Intelligence
  • Attributes of AI
  • AI myths and realities
  • Machine learning
  • Regression analysis
  • How AI is being applied

Chapter 2. Neural Networks

  • The limitations of basic machine learning
  • The problem with linear machine learning
  • Advantages of neural networks
  • Understand the neural network model
  • Potential use cases

Chapter 3. Machine Learning

  • Types of machine learning
  • Supervised vs unsupervised learning
  • Some ML terminology
  • Anomaly detection
  • Classification and clustering

Chapter 4. Ensuring Quality of ML Solutions

  • Lifecycle
  • Algorithm Selection
  • Precision, Recall & Accuracy
  • False Positives & Negatives

Chapter 5. Computer Vision

  • Image classification
  • Object detection
  • Image search
  • Scene description
  • Face APIs
  • Potential use cases

Chapter 6. Recommendation Systems

  • What is collaborative filtering
  • Explicit and implicit ratings
  • Working with sparse ratings data
  • The cold start problem
  • Online learning

Chapter 7. Natural Language Processing

  • Document classification
  • Sentiment Analysis
  • Question and answer
  • Chatbots
  • Speech Synthesis
  • Text to speech
  • Speech to text

Chapter 8. The Project Lifecycle

  • Defining business requirements
  • Defining learning types
  • Thinking about algorithms
  • Measurement and improvement
  • Building versus buy options

Appendix A. Vendor Solutions

  • Tooling to customize your ML domain
  • Tooling to auomate data analysis

Appencix B. AI in the Finance Industry

  • General Strategy
  • Case Studies

Appendix C. AI in Anti Money Laundering (AML) Compliance

  • AML Laws in US
  • Common Compliance Tasks - 1
  • Common Compliance Tasks - 2
  • Problem for the Banks
  • Hybrid Transaction Monitoring (TM) Solution
  • How AI is Used in AML Compliance
  • Rule vs. AI
  • Types of AI Models
  • Clustering Transactions
  • Typical Architecture

 

We regularly offer classes in these and other cities. Atlanta, Austin, Baltimore, Calgary, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Jacksonville, Miami, Montreal, New York City, Orlando, Ottawa, Philadelphia, Phoenix, Pittsburgh, Seattle, Toronto, Vancouver, Washington DC.