Table of Contents
Part of the course AI in the built Environment in IAAC 2024.
Length: approx. 20 hours.
Prerequisites: Intermediate Python knowledge (Datacamp courses: Introduction to Python, Intermediate Python).
In the end of the workshop: you should be able to understand the advantages and disadvantages of different ML models, being able to find and use them on tabular and image data as well as understand the logic of ML and its different phases.
These tutorials aim to give a gentle introduction to ML learning for students of Architecture and Urban Planning.
Open the notebooks/01_data_cleaning.ipynb in colab
- Intermediate Python knowledge (Datacamp courses: Introduction to Python, Intermediate Python)
- Run the cells one by one
- Read the comments
- Do the exercises
- If possible: read through the linked resources 😄
All teaching material is made available under a Creative Commons Attribution-ShareAlike 4.0 International licence.
In simpler words you can:
- share and distribute the material
- adapt the material to your needs: transform, mix and build upon it
Nevertheless you must:
- give appropriate credit
- provide the link to the license and the original material and indicate the changes that were made.
- distribute the material under the same license as the original or compatible ones
S. Fedorova, ML algorithms for architects, (2024), GitHub repository, https://github.com/STASYA00/IAAC2024_tutorials/
or use Github citation on the right of the page for APA or bibtex formats
Stasja - @stasya00 - e-mail - LinkedIn
- Angelos Chronis, Serjoscha Düring - the professors coleading the course and defining the scope
- IAAC - the university hosting the workshop
- University of Helsinki, in particular Automating GIS processes course 2023 the material from which was used for the quickstart #2
- My favorite README template