Executive Education: The New World of AI/ML

This repository is the source for the Executive Education on AI, Data Science and Machine Learning site, focused on what leaders need to understand to make good decisions in the new world shaped by AI/ML.

The course framing and many of the core ideas are inspired by Neil D. Lawrence’s book The Atomic Human (especially its emphasis on human limits and strengths: bandwidth, uncertainty, trust, and how we co-adapt with our tools).

• Website: built with Jekyll (GitHub Pages style)
• Course content: authored in _lamd/ and compiled into site pages/slides

What this course is

This is a short, executive-facing course that helps business leaders:

• build a practical mental model of what modern AI/ML can and can’t do
• understand how data quality, governance, privacy, and IP shape outcomes
• spot delivery risks early (teams, incentives, evaluation, project management)
• translate AI capability into strategy, operating model changes, and KPIs

It is explicitly grounded in themes developed in The Atomic Human, including:

• uncertainty: where models fail, how error propagates, and why governance matters
• trust: what you can safely delegate to machines, and what must remain a leadership responsibility
• intent and incentives: what systems optimise for vs what organisations mean

Where the materials live

• Source lectures: ./_lamd/*.md
• Generated lecture pages: ./_lectures/
• Generated slides: ./slides/

The ./_lamd/_lamd.yml file controls paths and build options.

Local development (website)

From the repo root:

bundle install
bundle exec jekyll serve

Then open the local server URL printed by Jekyll.

Building course artefacts (lamd pipeline)

The _lamd/compile.sh script compiles the lecture stubs listed in _lamd/lectures.csv.

Prerequisites

• pandoc
• inkscape (for some diagram conversions)
• a working Python (recommended: Python 3.12)

Create a local venv for this repo

cd .
/opt/homebrew/bin/python3.12 -m venv .venv
./.venv/bin/python -m pip install --upgrade pip setuptools wheel

Install lamd

Preferred (from PyPI):

./.venv/bin/pip install almd

If you’re developing against a local checkout instead:

./.venv/bin/pip install -e ../lawrennd/lamd

Compile the lectures

cd _lamd
PATH="$(pwd)/../.venv/bin:$PATH" ./compile.sh

License / reuse

Unless otherwise stated in individual files, content is provided for teaching and internal course use; reuse should preserve attribution.