[hybrid]R-Workshop: Machine Learning in R

This is an introductory workshop on how to use R for machine learning.

When: April 25th and May 2nd between 9:30-12:30hrs

Where: Where: Online and onsite. More info will be sent via email.

To register: Register here: https://forms.office.com/e/xBY9gP3upc

Requirements

• It is necessary to have R and RStudio installed before the session. You can do it from here: https://posit.co/download/rstudio-desktop/.

• It is necessary to know the basics of data handling and text analytics. In February and March, we covered these topics, so you can check the material here: https://github.com/SofiaG1l/R_Course/tree/master/R4SocialScientists

Learning Outcomes

• [April 25th] Unsupervised Machine Learning

  At the end of the session the students will be able to:

  1. Explain what machine learning is.
  2. Explain the difference between supervised and unsupervised learning.
  3. Perform dimensionality reduction.
  4. Visualize dimensionality reduction and interpret it.
  5. Perform clustering.
  6. Apply unsupervised machine learning to textual problem.

• [May 2nd] Supervised Machine Learning

  At the end of the session the students will be able to:

  1. Explain when to use linear regression and classification.
  2. Train a classifier using logistic regression, linear discriminant analysis, and K-nearest neighbors.
  3. Use resampling methods to validate their models.
  4. Use tree-based methods to classify.
  5. Apply supervised machine learning to a real problem.