Machine Learning using R

Dozierende	Dr. Yannick Rothacher, University of Zurich
Abschluss	Teilnahmebestätigung
Zielpublikum	R users from all professional groups.
Kosten	CHF 600.- für Angehörige der UZH/ETH und assoziierter Institute CHF 800.- für Alumni der UZH/ETH, Angehörige anderer Universtitäten, Einrichtungen der öffentlichen Hand und non-profit Organisationen CHF 1200.- für Firmen Personen ohne Anstellung können sich auf Anfrage zum UZH/ETH Preis anmelden.
Kurssprache	Englisch
Beschreibung	This course is an introduction to machine learning and its practical application in R. Machine learning techniques spread more and more in various areas of research and industry. In this course we aim at presenting the working principles of different machine learning methods in an intuitive manner, and discussing some of the general issues encountered in machine learning. We teach the application of the presented methods in R using hands-on exercises. The course will, therefore, consist of an alternation between theoretical lectures and practical exercises. The covered machine learning methods will mainly include k-means clustering, the k-nearest-neighbor algorithm, decision trees, random forests and neural networks. Amongst others, we will discuss general issues such as over- vs. underfitting, performance evaluation, the interpretability of machine learning methods and the advantages/disadvantages of machine learning methods compared to classical statistical models. The workshop is aimed at participants with a basic understanding of R (e.g. have already visited an introductory course in the past) but little or no previous experience in machine learning. Participants are required to bring their own laptop to the workshop with installed versions of R and RStudio. The learning goals are: 1) Participants gain a fundamental understanding of the goals and procedures in (mostly supervised) machine learning. 2) Participants understand the working principles of the presented machine learning methods. 3) Participants can apply the presented methods in R to data and interpret the results.
Daten	neuer Kurs: 9.-10. Dezember 2021
	Nach der Anmeldung erhalten Sie zunächst eine kurze automatische Anmeldebestätigung per Email. Wenn Sie diese Email erhalten haben, sind Sie erfolgreich und verbindlich zum Kurs angemeldet. Die schriftliche Rechnung wird aus administrativen Gründen erst ca. zwei Wochen vor Kursbeginn verschickt.