| Dozent/in |
Dr. Nicolas Attalides |
| Veranstaltungsart |
Workshop |
| Code |
HS261722 |
| Semester |
Herbstsemester 2026 |
| Durchführender Fachbereich |
Diverse |
| Studienstufe |
Master
Doktorat |
| Termin/e |
Fr, 02.10.2026, 09:30 - 16:30 Uhr
Sa, 03.10.2026, 09:30 - 16:30 Uhr
Fr, 09.10.2026, 09:30 - 16:30 Uhr
Sa, 10.10.2026, 09:30 - 16:30 Uhr |
| Umfang |
Blockveranstaltung |
| Inhalt |
Machine Learning is an extremely popular topic within the field of Artificial Intelligence. We encounter the results of machine learning algorithms daily, for example, when we play online games or do online shopping to applying for an insurance or a loan.
One way to define machine learning is the intersection between statistics and computer science. The R programming language is perfectly positioned to handle both fields. It offers a huge variety of statistical analysis solutions with over 22,000 packages which include a wide array of machine learning implementations. For example, one can apply a Boosting and Gradient Descent algorithm, build a Random Forest model, or design a Neural Network.
|
| Lernziele |
This course focuses on introducing the participants to the main components of implementing machine learning in R. The course is structured to cover the following topics:
• Get started with Machine Learning
• Machine Learning and R
• Explore and prepare the data
• Design Machine Learning workflow
• Classification problems
• Model specification and data pre-processing
• Machine Learning algorithms (Decision Trees, Random Forest, XGBoost, Neural Network*)
• Resampling
• Hyper-parameter tuning
• Unsupervised Machine Learning (Clustering)
• Other topics* such as Feature Engineering, Variable Importance, Parallelisation and Random Grid
(*if timings allow)
|
| Voraussetzungen |
Course participants are expected to have a good working knowledge of the R programming language. It is assumed that participants have some prior experience in basic data analysis (such as data manipulation and visualisation) and a basic understanding of statistics. No prior knowledge of machine learning theory is required.
Participants should have their own laptop with R, RStudio and the relevant packages installed. |
| Sprache |
Englisch |
| Begrenzung |
Only Master or doctoral students (postdoctoral researchers can sign up via lumacss@unilu.ch). Priority for LUMACSS students if there are too many registrations. |
| Anmeldung |
***Important*** To earn credits, you must register for the course via UniPortal. Registration is open from two weeks before to two weeks after the start of the semester. You can withdraw from the course after this period by notifying the lecturer and lumacss@unilu.ch. You can find the registration details here: http://www.unilu.ch/ksf/semesterdaten |
| Leistungsnachweis |
There is no examination. Participants are expected to actively engage in the hands-on components of the workshop. Successful participation is awarded 2 ECTS. |
| Abschlussform / Credits |
Bestätigte Teilnahme / 2 Credits
|
| Hörer-/innen |
Nein |
| Kontakt |
lumacss@unilu.ch |
| Material |
Instructions for the technical setup will be circulated via e-mail by the instructor during the week before the (first session of the) course. Learning material such as slides, code and solutions to exercises will be circulated by the instructor after the course. |