Machine Learning using R

Dozierende	Dr. Yannick Rothacher, University of Zurich
Abschluss	Confirmation of participation
Zielpublikum	R users from all professional groups.
Kosten	CHF 600.- for members of UZH/ETH and associated institutes CHF 800.- for alumni of UZH/ETH, members of other universities, the public sector and non-profit organizations CHF 1200.-for companies Persons without current employment can register for the UZH/ETH fee upon request.
Kurssprache	English
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	New course: 9.-10. Dezember 2021
	After registering you will receive a short automatic confirmation by email. If you received this email you are successfully and bindingly registered for the course. For administrative reasons the written invoice won't be sent out until about two weeks before the course.