AI foundations for business professionals

Full course outline:

Module 1: Demystifying AI

Lecture 1

  • A term with any definitions

  • An objective and a field

  • Excitement and disappointment

Lecture 2: 

  • Introducing prediction engines

  • Introducing machine learning

Lecture 3

  • Prediction engines

  • Don’t expect ‘intelligence’ (It’s not magic)

Module 2: Building a prediction engine

Lecture 4: 

  • What characterizes AI? Inputs, model, outputs

Lecture 5:

  • Two approaches compared: a gentle introduction

  • Building a jacket prediction engine

Lecture 6:

  • Human-crafted rules or machine learning?

Module 3: New capabilities… and limitations

Lecture 7

  • Expanding the number of tasks that can be automated

  • New insights –> more informed decisions

  • Personalization: when predictions are granular… and cheap

Lecture 8:

  • What can’t AI applications do well?

Module 4: From data to ‘intelligence

Lecture 9

  • What is data?

  • Structured data

  • Machine learning unlocks new insights from more types of data

Lecture 10

  • What do AI applications do?

  • Predictions and automated instructions

  • When is a machine ‘decision’ appropriate?

Module 5: Machine learning approaches

Lecture 11

  • Three definitions

Machine learning basics

Lecture 12

  • What’s an algorithm?

  • Traditional vs machine learning algorithms

  • What’s a machine learning model?

Lecture 13

  • Machine learning approaches

  • Supervised learning

  • Unsupervised learning

Lecture 14

  • Artificial neural networks and deep learning

Module 6: Risks and trade-offs

Lecture 15:

  • Beware the hype

  • Three drivers of new risks

Lecture 16

  • What could go wrong? Potential consequences

Module 7: How it’s built

Lecture 17

  • It’s all about data

Oil and data: two similar transformations

Lecture 18

  • The anatomy of an AI project

  • The data scientist’s mission

Module 8: The importance of domain expertise

Lecture 19:

  • The skills gap

  • A talent gap and a knowledge gap

  • Marrying technical sills and domain expertise

Lecture 20: What do you know that data scientists might not?

  • Applying your skills to AI projects

  • What might you know that data scientists’ not?

  • How can you leverage your expertise?

Module 9: Bonus module: Go from observer to contributor

Lecture 21

  • Go from observer to contributor

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