Machine Learning for Big Data

Welcome! Machine Learning for Big Data (MLBD) is a teaching repository for the Machine Learning for Big Data course. You can find more information on the following pages:

• Course page
• Syllabus
• Teaching notes

Key files

• course.yml is the single source of truth for course metadata, the calendar synchronization configuration, synchronized events, and Quarto variables.
• template/sources.yml records reviewed source repositories and the template update process.

Common commands

• make site renders exercises and the website.
• make pdfs creates every slide PDF.
• make _site/slides/session_03.pdf creates one slide deck.
• make all renders the complete website and all slide PDFs.
• make exercises creates assignment and solution variants.
• make sync-events reads the authoritative handbook YAML URL configured only under schedule.source in course.yml, imports calendar-controlled date, time, location, and external ID fields into course.yml, and preserves manually maintained event metadata. The website calendar renders those synchronized course.yml events; it does not fetch the handbook itself.
• make clean removes generated build artifacts.

Slide PDF prerequisites

Install Quarto and Docker. Quarto renders the temporary Reveal.js presentation on the host; the Makefile automatically builds a Docker image containing the pinned Decktape version, Chromium dependencies, and Ghostscript. Node.js, npm, and Decktape do not need to be installed on the host.

Build all slide PDFs with make pdfs, build an individual deck with make _site/slides/session_03.pdf, or build the site and every PDF together with make all. Generated PDFs are written as the current host user.

R dependencies

The CRAN packages used by executable R chunks are listed in r-packages.txt. Install any packages that are not already available before rendering the course materials:

Rscript scripts/install_r_dependencies.R

The helper leaves existing installations unchanged and installs only missing packages. Display-only R examples (fenced with r rather than {r}) are not executed during rendering and may demonstrate additional, optional packages.

License

The teaching contents are licensed under the CC BY 4.0 License unless noted otherwise.