From 2 November 2020, the autumn semester 2020 will take place online. Exceptions: Courses that can only be carried out with on-site presence.
Please note the information provided by the lecturers via e-mail.

401-6245-00L  Data Mining

SemesterAutumn Semester 2016
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.


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.
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!).