index.qmd

---
title: "Statistical Principles in Machine Learning for Small Biomedical Data"
---

Date: **Monday 11 December 2023, 9:00-12:00**

Room: **Perl (room 2453), Ole-Johan Dahls hus (OJD)**

Instructors: Manuela Zucknick (main), Theophilus Asenso

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# 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](https://github.com/ocbe-uio/workshop-stat-highdim).

#### 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](part0_prep.qmd)                                  |                   |
| 9:00 - 10:00  |     [(Supervised) machine learning with small data](/lecture_notes/StatPrinciples_ML_1.pdf)     | Manuela Zucknick  |
|               |                                    [R lab 1](part1_eda.qmd)                                     | Manuela Zucknick  |
| 10:15 - 11:15 |       [Overfitting, regularisation and all that](/lecture_notes/StatPrinciples_ML_2.pdf)        | Manuela Zucknick  |
|               |                                   [R lab 2](part2_model.qmd)                                    | Manuela Zucknick  |
| 11:30 - 12:00 | [Hierarchical models and structured penalties](/lecture_notes/Theo.pdf) | Theophilus Asenso |