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[mmaclow]

Course goals/innovation: (10/10) reproduction and extension of a published machine learning model applied to Kepler data

Technical challenge: (9/10) sophisticated use of production-grade neural network tools to prepare data and choose hyperparameters prior to assembly of a complex neural network.

Implementation quality: (15/15) Two different approaches successfully implemented: both binary and multiclass.

Code documentation/reproducibility: (11/15) more comments at function level explaining purposes and goals would make code clearer to read and easier to maintain.

Writeup motivation: (10/10) clear summary of existing work with appropriate references to the relevant literature, along with good justification for the choice of model to reproduce. Clear description of model architecture.

Writeup results: (28/30) Overall an exemplary presentation. However, more explanation of machine-learning specific terminology is important for presenting to domain-specific specialists. Loss curves, AUC scores and other performance metrics are critical to understand, but not necessarily widely known outside of the ML community, so their implications should be made explicit when reporting results.

Writeup lessons learned/future: (10/10) Clear placement of results in context, particularly in the comparison with ExoMiner.

Total: (93/100) Outstanding work!