Instructor: Nikhil Bhagwat & Mohammad Torabi
Outline
- Define machine-learning nomenclature
- Describe basics of the “learning” process
- Explain model design choices and performance trade-offs
- Introduce model selection and validation frameworks
- Explain model performance metrics
Questions you will be able to answer after taking this module
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When is ML a useful approach?
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Supervised learning
- Model training - what is under/over-fitting?
- Model selection - what is (nested) cross-validation?
- Model evaluation - what are type-1 and type-2 errors?
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What NOT to do when using ML models in your research
Things you will NOT learn in this module (if you are an advanced ML student)
- In-depth review of unsupervised learning approaches (e.g. clustering)
- How train deep-learning models
- How to use and/or defeat chatGPT
Material
Resources
IMPORTANT! To fully understand the material taught in this module, you should make sure that you are already familiar with the following concepts (please take some time to review them if needed):
- Basics of linear algebra (check out these videos if you need a refresher)
- Do you know how to use vectors?
- Do you know how to multiply two matrices?
- Basics of linear regression
- Do you know what a mean-square error is?
- How to fit linear regression or GLMs?
