Overview

Author

Huanfa Chen

Published

March 25, 2026

Objectives

  1. Get an overview of (geospatial) machine learning.
  2. Understand framework and key methods of supervised learning.
  3. Can apply supervised learning to real-world problems with various data types (tabular, image, network)

Why study this module?

  • AI can make mistakes; understanding how it works is crucial.
  • Achieving a balance between predictive accuracy and interpretability is important.
  • Spatial is special: spatial data have unique characteristics that require special treatment.

Prerequisites

  • Can understand/follow Python code and run Jupyter notebooks.
  • Basic knowledge of statistics (mean, variance, correlation, probability) and linear regression.

Course Structure

  • Lectures: 10 weeks (Wednesdays 9:00–10:30).
  • Tutorials: 10 weeks (Wednesdays 10:30–12:00).

Platforms

  • Email for important notices and private questions.
  • Github & website for lecture notes and notebooks.
  • Moodle for lectures recording and assessments.
  • Slack for public questions.

Weekly Schedule

Session Topic Suggested reading
1 Intro machine learning IMLP Ch 1
2 Supervised learning metrics IMLP 5.3, APM Ch 16
3 Supervised learning workflow IMLP Ch2.1-2.3.2, APM Ch 4-4.3, IMLP Ch 5.1, 5.2, APM Ch 4.4-4.8
4 Tree-based methods IMLP 2.3.5, 2.3.6, APM Ch 14.1-14.4
5 Neural networks IMLP Ch 2.3.8, DL Ch 6, Ch 7.8
6 Graph neural networks tkipf.github.io
7 Model interpretation & feature selection IML, LIML
8 Imbalanced data APM Ch16, SMOTE, Easy Ensembles
9 Machine learning operations Madewithml.com
10 Testing ML systems Madewithml.com

References

  • (COMS) COMS W4995 Applied Machine Learning Spring 2020 link
  • (IMLP) Mueller, Guido - Introduction to machine learning with python. link, free for UCL users
  • (APM) Kuhn, Johnson - Applied predictive modeling link
    1. Goodfellow, Bengio, Courville - Deep Learning. link
  • (PDSH) Python Data Science Handbook by Jake VanderPlas link
  • (IML) Interpretable Machine Learning by Christoph Molnar link
  • (LIML) Limitations of Interpretable Machine Learning link

Attendance Recording

  • 70% attendance required for student visa holders.
  • Please attend all lectures and workshops.
  • Contact module lead & bartlett.pg-casa@ucl.ac.uk, if you can’t attend.

Assessment

Assessment

  • Python notebook (summative): 100%
  • Weekly quiz (formative)

UCL Assessment Policy

  • All submissions via Moodle, not emails.
  • Late penalties: Up to 48h (-10 points); up to 7 days (capped at 50); over 7 days (scores 0).
  • DAP or Extenuating circumstances: to submit on Portico.
  • Avoid plagiarism and unverified references

Moodle Feedback

Please provide anonymous feedback on Moodle for every week.

Github Feedback

You can also give feedback on Github issues.