Foundations for Machine Learning

Dec. 19, 2021
Kris Sankaran

Model Evaluation and Visualization

Communicating model behavior after fitting them.
Dec. 18, 2021
Kris Sankaran

Overfitting, Bias-Variance, and Cross-Validation

Choosing models with the right complexity.
Dec. 17, 2021
Kris Sankaran

Preprocessing and Featurization
Dec. 16, 2021
Kris Sankaran

Setting Up Supervised Learning Probems
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Foundations for Machine Learning

Notes for the morning session for day 2 of NAAMII’s Winter School on AI.

Learning Outcomes

Binder

  1. Given a problem description, determine features that may be relevant, including those directly present in the raw data and those that must be constructed.
  2. Given a problem description, determine an appropriate response variable.
  3. Design a model evaluation scheme for a specific problem / model context, keeping in mind the dangers of overfitting and the bias-variance trade-off.
  4. Given a covariate / response pair, use visualization and summary statistics to identify preprocessing or transformation methods that may improve downstream model performance.
  5. Discuss the relative merits of linear, sparse, and tree-based methods in a particular problem setting, and prepare code that implements them appropriately.
  6. Use model summaries and data visualization to summarize the important features in a model and note areas for potential improvement.

Exercises

Exercises from this session are available here.