Event Description
This workshop can be attended either on-site (in Amsterdam) or online.
The main purpose of this workshop is to familiarize participants with key concepts in machine learning, assisted by the machine learning module in JASP (for a quick impression see the blogposts here, here, here, and here). The workshop covers popular machine learning techniques such as k-nearest neighbors (KNN), random forests, and boosted regression trees. The techniques are then applied to concrete data sets. Additionally, the workshop explains how machine learning models, once trained, can generate predictions for new data sets. The workshop is designed to provide an accessible introduction to machine learning in JASP, emphasizing its user-friendly interface and powerful analytical capabilities. Through a combination of hands-on exercises and guided discussions, participants will learn how to implement, interpret, and evaluate machine learning models with ease.
Target audience
At the end of this workshop, participants should have the knowledge to confidently apply the machine learning techniques in JASP to new data sets, interpret the output, and report the results. This workshop is relevant to anybody who desires an accessible introduction to machine learning. No background in statistics or computer programming is required.
Participants should bring a laptop with the latest version of JASP installed. Plenary sessions will be given by Don van den Bergh and Koen Derks.
We aim to make use of a number of tools to create a smooth, fun, and interactive learning experience. For those participating online, lectures will be held on Zoom, and video recordings will be made available to workshop participants. Moreover, you will be invited to an online workspace where our team will assist you and answer any questions you might have about the materials.
We strive to lower barriers to workshop participation based on cost for researchers around the world who may be unable, or have limited ability, to cover the workshop fees. If you lack funding despite exhausting all alternative funding sources and struggle to pay the workshop fees, you may be eligible for a (partial) fee waiver. You can contact us at events@jasp-stats.org.
Preliminary program
Note that the times below are given in Central European Summer Time (GMT+2). We are trying our best to accommodate everyone’s needs despite the different time zones by offering video recordings and personal support.
Wednesday, August 26th
09:00 – 09:30: Registration
09:30 – 10:00: Welcome and Introduction to Machine Learning
10:00 – 10:30: Lecture: Exploring a typical classification workflow
10:30 – 11:15: Exercises: Applying classification and making predictions for new data
11:15 – 11:30: Break
11:30 – 12:00: Lecture: Exploring a typical regression workflow
12:00 – 12:45: Exercises: Applying regression and making predictions for new data
12:45 – 13:45: Lunch Break (on your own)
13:45 – 14:15: Lecture: Exploring a typical clustering workflow
14:15 – 15:00: Exercises: Applying clustering
15:00 – 15:15: Break
15:15 – 15:45: Lecture: Fairness and discrimination in Machine Learning
15:45 – 16:30: Exercises: Evaluating fairness in classification models
16:30 – 17:00: Q&A and Wrap-Up
Information for students
We offer a special discount for students and award a certificate of participation indicating the total number of hours spent on the workshop. Students may inquire with their institution about earning equivalent European Credits (ECs) towards their degree. Participation in this workshop is worth 1 ECs (28 h). In order to be eligible for these ECs, students should prepare for the workshop by reading the following background literature:
Show literature list
- Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). New York: Springer.
Workshop Location
REC A2.10
Roeterseilandcampus
Roetersstraat 11
Amsterdam (NL)
