Artificial Intelligence Fundamentals

Table of Contents

1. Introduction

What is Artificial Intelligence?

Why is it important?

Analytics Maturity Curve

2. AI Usages

Business Cases:

Automotive

Retail

Manufacturing

Health

Finance and Banking

Academia and Education

...

AI for Good

3. AI Infrastructure

Algorithms - the top 10

Hardware

Software: frameworks

Summary

4. AI Capability

5. Analytics Assessment

Self-Assessment Process

Assessment Teams

The Assessment Process

The Self-Assessment Framework

Section 1. Management Commitment

Section 2. Business Responsibility

Section 3. AI Competency

Section 4. Linking it to your Values - AI for good

Scoring the Self Assessment

Developing an Action Plan

Moving to the Next Maturity Level - from spreadsheets to prediction?

AI Support

Impact of AI

The Importance of Assessment

Summary

6. Launching AI - Getting Started

Gaining Management Commitment

Create and Communicate a Vision Develop an Innovation Strategy

Measure Business Value

Developing Innovation Capabilities

Skills - make or buy

Training

Assets, Methods, Skillsets

Develop Infrastructure

Enablers and Inhibitors

Summary

7. Deep Learning (DL)

DL in action

DL history

DL vs classical ML

DL frameworks

DL in the Cloud - case study Neon introduction

8. AI Basics

Multi-layer perceptions (MLP)

Activations

Initializations

Costs

Optimizers - Gradient descent and variants

Backpropagation

Hands on exercise

9. Convolutional Neural Networks

CNN overview / examples

Layers

Convolutions

Pooling

Dropout

Local response normalization

De-conv

Popular CNNs

Hands on exercise

Appendix A Case Studies

Appendix B Assessment Tool

Self-Assessment Tool

Section 1. Management Commitment

Section 2. Business Responsibility

Section 3. Competency

Section 4. Enterprise Values

Section 5. Support

Section 6. Impact

Appraising Performance

Appendix C hands on examples - links and code

References