Dive deeper into the aspects covered in this lesson with these readings that expand on the topics discussed. Each reading includes reflective questions to help you focus on key learning points and strengthen your understanding.

Reading: Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison.
By André Pfob, Sheng-Chieh Lu & Chris Sidey-Gibbons.

In this reading , the authors walk you through key techniques for building high-quality machine learning (ML) systems, including data pre-processing (cleaning and transforming raw data into a format suitable for analysis), hyperparameter tuning (finding the best settings for your model to improve performance), and model comparison (evaluating different models to choose the one that works best), using open-source software and data.

Reading time: ~60 min

   Download the reading

Reflective questions

  • What is the correct way to display the performance of a learning algorithm?
  • What are the more risky biases affecting the use of machine learning in medicine?
  • What are the key steps in data preprocessing and why are they so critical?

Source: Pfob, A., Lu, SC. & Sidey-Gibbons, C. Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison. BMC Med Res Methodol 22, 282 (2022).  Link

Reading: An Introduction to Statistical Learning.
By Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor.

If you’re getting curious about machine learning and want to explore further, this book is a great go-to reference. It covers key concepts, essential theory, and a wealth of examples, along with hands-on exercises in Python and R. We’re highlighting it for you in case you’re excited to dig deeper and take your understanding to the next level!

   Download the reading (R)

   Download the reading (Python)

Source: James et al. (2021). An Introduction to Statistical Learning. Springer.  Link