##### AM-06 - Map algebra

- Explain the categories of map algebra operations (i.e., local, focal, zonal, and global functions)
- Explain why georegistration is a precondition to map algebra
- Differentiate between map algebra and matrix algebra using real examples
- Perform a map algebra calculation using command line, form-based, and flow charting user interfaces
- Describe a real modeling situation in which map algebra would be used (e.g., site selection, climate classification, least-cost path)
- Describe how map algebra performs mathematical functions on raster grids

## AM-94 - Machine Learning Approaches

Machine learning approaches are increasingly used across numerous applications in order to learn from data and generate new knowledge discoveries, advance scientific studies and support automated decision making. In this knowledge entry, the fundamentals of Machine Learning (ML) are introduced, focusing on how feature spaces, models and algorithms are being developed and applied in geospatial studies. An example of a ML workflow for supervised/unsupervised learning is also introduced. The main challenges in ML approaches and our vision for future work are discussed at the end.