401-6245-00L Data Mining
|Semester||Autumn Semester 2016|
|Periodicity||two-yearly recurring course|
|Language of instruction||German|
|Comment||Special 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 firstname.lastname@example.org. The Registrar's Office will then register you for the course.|
|Abstract||Block 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.
|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 notes||The block course is based on (German language) lecture notes.|
|Prerequisites / Notice||The 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!).