Statistical Principles in Machine Learning for Small Biomedical Data
Date: Tuesday 13 December 2022, 9:00-12:00
Room: Python (room 2269), Ole-Johan Dahls hus (OJD)
Instructors: Manuela Zucknick (main), Theophilus Asenso and Chi Zhang
Welcome!
- The goal of the workshop is to introduce kep concepts in machine learning, such as regularisation.
- The workshop is intended for students and researchers who are interested in applying machine learning methods to small data (few samples, but potentially many features) or noisy data (e.g. biomedical data)
- Workshop material can be found in the workshop github repository.
Learning Objectives
At the end of the tutorial, participants will be able to
- understand key concepts for training machine learning models such as regularisation;
- understand how to incorporate data structure in the regularisation process.
Pre-requisites
- Basic familiarity with R
- Introductory level statistics, including regression
Schedule
| Time | Topic | Presenter |
|---|---|---|
| Now | Preparations | |
| 9:00 - 10:00 | (Supervised) machine learning with small data | Manuela Zucknick |
| R lab 1 | Manuela Zucknick | |
| 10:15 - 11:15 | Overfitting, regularisation and all that | Manuela Zucknick |
| R lab 2 | Manuela Zucknick | |
| 11:30 - 12:00 | Outlook: Hierarchical models and structured penalties | Theophilus Asenso |