447-6245-00L  Data Mining

SemesterAutumn Semester 2020
LecturersM. Mächler
Periodicitytwo-yearly recurring course
Language of instructionGerman
CommentSpecial Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to registrar@ethz.ch. The Registrar's Office will then register you for the course.



Courses

NumberTitleHoursLecturers
447-6245-00 GData-Mining Special students and auditors need a special permission from the lecturers.
Blockkurs. Weitere Informationen unter http://stat.ethz.ch/wbl/wbl
14s hrs
Mon/114:15-16:00HG E 1.2 »
16:15-18:00HG E 5 »
02.11.14:15-16:00HG E 1.2 »
16:15-18:00HG E 5 »
M. Mächler

Catalogue data

AbstractBlock course only on prediction problems, aka "supervised learning".

Part 1, Classification: logistic regression, linear/quadratic discriminant analysis, Bayes classifier; additive and tree models; further flexible ("nonparametric") methods.

Part 2, Flexible Prediction: additive models, MARS, Y-Transformation models (ACE,AVAS); Projection Pursuit Regression (PPR), neural nets.
Learning objective
Content"Data Mining" is a large field from which in this block course, we only treat so called prediction problems, aka "supervised learning".

Part 1, Classification, recalls logistic regression and linear / quadratic discriminant analysis (LDA/QDA) and extends these (in the framework of 'Bayes classifier") to (generalized) additive (GAM) and tree models (CART), and further
mentions other flexible ("nonparametric") methods.

Part 2, Flexible Prediction (of continuous or "class" response/target) contains additive models, MARS, Y-Transformation models (ACE, AVAS); Projection Pursuit Regression (PPR), neural nets.
Lecture notesThe block course is based on (German language) lecture notes.
Prerequisites / NoticeThe exercises are done exlusively with the (free, open source) software "R"
(http://www.r-project.org). A final exam will also happen at the computers, using R (and your brains!).

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits1 credit
ExaminersM. Mächler
Typeungraded semester performance
Language of examinationGerman
RepetitionRepetition possible without re-enrolling for the course unit.

Learning materials

 
Main linkSkripts, Infos, etc
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

General : Special students and auditors need a special permission from the lecturers

Offered in

ProgrammeSectionType
CAS in Applied StatisticsFurther CoursesWInformation
DAS in Applied StatisticsElectivesWInformation
Statistics MasterStatistical and Mathematical CoursesWInformation