KKU Datathon 2018
Mon, 17 September 2018 (1-4PM)
Calvin J Chiew, Arporn Wangwiwatsin
This workshop aims to introduce clinicians to popular statistical methods used in machine learning, without delving into the underlying mathematical theory. We will focus on the random forest and support vector machine for classification, as well as general concepts of model fit and cross-validation. In the hands-on exercise, you will be asked to implement and evaluate these models on a clinical prediction problem. No prior programming experience is assumed. Basics of the Python language and Jupyter notebook environment will be covered.
- Laptop
- Anaconda 5.0.1 (Python 3.6 version) installed
- Download the installer for Anaconda (Python 3.6 version) on your respective OS (Windows/Mac/Linux) from https://www.anaconda.com/download/.
- Run the installer and use all default options when prompted.
- After installation is complete, make sure you can open Anaconda Navigator and launch Jupyter notebook.
| No. | Section | Links |
|---|---|---|
| (1) | Lecture | Slides, Handout |
| (2) | Python | Dataset, Notebook, Solutions |
| (3) | Sample | Dataset, Notebook |
| (4) | Exercise | Dataset, Notebook, Solutions |
- An Introduction to Statistical Learning with Applications in R by G James, D Witten, T Hastie & R Tibshirani (Springer 2013)