An introduction to machine learning in R focusing on classification (supervised learning)
Frequency: Twice a year
Time: 2 x 3 hours
Language: English or Norwegian
Type of course: In person
Target audience:
UiO reseachers and students who want to get started with machine learning in R.
A video (approximately 25 minutes) has been prepared that might be useful for those that are completely new to machine learning, with example use-cases in research.
Prerequisites:
It is an advantage but not necessary that you are accustomed to writing code in R. Basic knowledge of descriptive statistics and tidyverse is a plus.
Contents:
- Exploratory data analysis
- Binary classification
- Feature importance
- Multiclass classification
- Cross-validation
- Additional topics
- Preprocessing data with "recipe"
- Building and evaluating multiple models
simultaneously - Statistically comparing models
- Hyperparamater tuning
- Predicting a continuous variable
Briefly about the course:
The focus will be on building and evaluating machine learning models in R rather than an in-depth breakdown of specific algorithms. We will be building models to distinguish between different categories of text based on linguistic features (including number of nouns, adjectives, etc.) using XGBoost.
Contact
Send questions about the course to:
statistikk@usit.uio.no