AI foundations for business professionals , A code-free intro to artificial intelligence, ML, & data science for professionals, marketers, managers, & executives.
What you”ll learn:
- This course provides students with a broad introduction to AI, and a foundational understanding of what AI is, what it is not, and why it matters.
- The main differences between building a prediction engine using human-crafted rules and machine learning – and why this difference is central to AI.
- Three key capabilities that AI makes possible, why they matter, and what AI applications cannot yet do.
- The types of data that AI applications feed on, where that data comes from, and how AI applications – with the help of ML – turn this data into ‘intelligence’.
- The main principles behind the machine learning and deep learning approaches that power the current wave of AI applications.
- Artificial neural networks and deep learning: the reality behind the hype.
- Three main drivers of risks which are characteristic of AI, why they arise, and their potential consequences in a workplace environment.
- An overview of how AI applications are built – and who builds them (with the help of extended analogy).
- Why one of the biggest problems the AI industry faces today – a pronounced skills gap – represents an opportunity for students.
- How to use their own knowledge, skills and expertise to provide valuable contributions to AI projects.
- Students will learn how to build upon the foundations they learned upon in this course, to make the move from informed observer to valuable contributor.
Course Description
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
