Introduction to Machine learning in R: Classification

An introduction to machine learning in R focusing on classification (supervised learning)

Time and place:
The course consists of two sessions:

Wednesday April 17th, 09:15-12:00, in seminar room Prolog, Ole-Johan Dahls hus

Friday April 19th, 09:15-12:00, in seminar room Prolog, Ole-Johan Dahls hus

Language: 
English

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
Profilbilde
Instructor:?
Luigi Maglanoc

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 l