Machine Learning for the Geosciences
Welcome to the course website for the Winter 2026 iteration of ESS 469/569!
- Syllabus
- Class notes
- March 09 | Assessing ML workflow performance
- March 06 | ML best practices
- March 04 | Assembling the transformer
- March 02 | Attention and the transformer
- February 27 | Physics-informed neural networks
- February 25 | Neural networks: worked examples
- February 23 | Neural networks: continued
- February 18 | Neural networks: more architectures
- February 13 | Neural networks (beginning with the perceptron)
- February 11 | Logistic regression
- February 09 | Gradient descent
- February 04 | Classification, continued
- February 02 | Classification
- January 30 | Clustering
- January 28 | Fundamental concepts in AI/ML
- January 26 | Dimensional Reduction
- January 21 | Filtering and resampling
- January 14 | Data characterization
- January 14 | Accessing data
- January 12 | Data: Types, formats, and availability
- January 09 | Introductions
- January 07 | Putting together your coding ecosystem
- January 06 | An introduction to ESS 469/569
- Assignments
- * | Final project: Presentation
- * | Final project: Multipanel Figure
- * | Final project: A single Markdown file
- 09 | Embeddings
- 08 | The curse of dimensionality
- 07 | Third team presentation
- 06 | Second team presentation
- 06 | PCA, applied
- 05 | First team presentation
- 04 | Washington flood data
- 03 | Version control
- 02 | Student introductions
- 01 | Setup
- Course repository