Topic outline

  • General

    Introduction to Classification

    Lausanne, 18 November 2014

    Overview

    This course will introduce the basics of predictive modeling (“classifier construction”). We will discuss some of the most common types of classifiers, such as classification trees, random forests, support vector machines, discriminant analysis and K-nearest neighbor classifiers. We will also discuss measures and strategies for evaluating the performance of a classifier appropriately using cross-validation.

    Objectives

    The objective of this course is to introduce the participants to the basic concepts in classifier construction and evaluation. After successful completion of this course, the participants will be able to use R to construct and evaluate the performance of several different types of classification models.

    Requirements

    Skill requirements: basic knowledge of R

    Computer requirements: laptop with R installed

    Application

    The registration fees for academics are 50 CHF. This includes course content material and coffee breaks.Participants from non-academic institutions should contact us before application.

    Application is closed.

    Deadline for application and cancellation is set to the 11th of November 2014.

    Location

    Genopode Building, room 2020

    Additional information

    The course will be taught by Charlotte Soneson, PhD.

    This course does not provide ECTS credits.

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