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