Machine learning is on every agenda. Few leaders were ever taught it.
Your teams speak in models.
The decisions those models drive still land on your desk.
Vendors sell accuracy.
The right questions tell you what their claims are worth.
Boards expect a point of view.
Understanding the ML helps you form your own.
When someone says “random forest,” know what it means, and what to ask next.
Private machine learning tutoring for executives who want to understand the technology behind the decisions they make. No coding background required.
“We’ve moved the credit model to a random forest. Accuracy is up considerably.”
“Up against what baseline? And how does it perform on data it has never seen?”
From nodding along to leading the conversation.
Speak the language
Random forests, neural networks, overfitting, embeddings: what each one is, and where it breaks.
Question the numbers
Ask for the baseline, the test data and the cost of being wrong.
Evaluate vendors
Tell what is genuinely new from familiar analytics with a new label.
Guide your teams
Give data scientists feedback they recognise as informed.
Get under the hood
Build simple models yourself, because doing it makes the concepts concrete.
Lead the strategy
Judge where machine learning creates real value, and where it does not.
Seven focused hours, scheduled around you.
After teaching this material 23 times to nearly 1,700 participants, I’ve learned what needs explaining, what makes the ideas click, and what you simply don’t need.
Everything you need. Nothing you don’t.
Made simple, never simplistic.
Examples come from your role, your industry and the decisions in front of you. We build models together on a shared screen, with no prior coding needed.
- Private, one-to-one
- Your schedule
- Zoom, Teams, Webex or Meet
- Strictly confidential
Real machine learning, selected for relevance.
Tailored to you. A typical sequence:
- 01
Foundations
Your first Python code, and how models learn from data.
- 02
Classical models
Regression and nearest neighbours: the dependable baselines behind everyday decisions.
- 03
Trees and forests
Decision trees and random forests, and when they work.
- 04
Neural networks and explainability
What happens inside them, and how their decisions can be explained.
- 05
Testing models
Cross-validation, overfitting, and how to tell a robust result from a lucky one.
- 06
LLMs
How they work, where they fail, and what that means in practice.
- 07
Agentic AI
What agents actually do, and how to separate capability from a polished demo.
Academic depth, explained in the language of business.
A former finance professor at the University of Bern and Frankfurt School of Finance & Management, with years of experience teaching machine learning to finance professionals and business students, including many who began with no coding experience, and a few who weren’t entirely convinced they wanted any.
The approach is practical, patient and hands-on. Even the sceptics have been known to enjoy it.
PhD in Finance (EPFL and Swiss Finance Institute) · CFA · PRM
Walk into your next AI discussion with the right questions.
Start with a private introductory conversation.
Book a private consultationor write to private@augmentedcsuite.com