Full course outline:
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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