susanna.roeblitz@zib.de

ZIB, Takustraße 7, Raum 4104

Office hours: by email appointment Annalisa Marsico

annalisa.marsico@fu-berlin.de

Takustr. 9, Raum 011

Office hours: by email appointment Alena van Bömmel

mysickov@molgen.mpg.de

MPI, Ihnestraße 63-73, Raum 3.3.83

Anna Ramisch

ramisch@molgen.mpg.de

MPI, Ihnestraße 63-73, Raum 3.3.83

Lisa Barros de Andrade e Sousa

lisasous@molgen.mpg.de

MPI, Ihnestraße 63-73, Raum 3.3.83

According to the information we got from the "Prüfungsbüro", there is no "Freischussregelung" any longer. That means, if you passed the first exam, you are not allowed to take the second one in order to improve your mark. This applies to all lectures that are described in the study and examination rules of the BSc and MSc Bioinformatics as well as the MSc Mathematics.

20.4. Here the results from the Nachklausur Results_Exam. To look at your graded exam please come to Annalisa Marsico's office (but write an email before).

The 2nd exam takes place on April 14, 12:15-13:45, also in the ZIB Lecture Hall.

SWS: 2

Exercises: Alena van Bömmel, Lisa Barros de Andrade y Sousa, Anna Ramisch

SWS: 2

ECTS: 6

Language: English

Thursday 12-14h, Takustr. 9, SR 006/T9

First lecture 15.10.

Exercises:

Monday 12:15 - 13:45, Arnimallee 6, SR 032

Monday 14:15 - 15:45, Arnimallee 6, SR 025/026

Mathematical background for Markov chains and related topics.

A. Marsico:

Computational Statistics and Statistical Learning

Pierre Bremaud. Markov Chains, Gibbs Fields, Mote Carlo Simulation, and Queues. Springer 1999.

Ehrhard Behrends, Introduction to Markov Chains (with Special Emphasis on Rapid Mixing), Vieweg, 1999.

Hastie, Tibshirani & Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2009. Available here.

The provided lecture notes

01 Introduction and basic definitions. Notes

02 Canonical representation and n-step transition. Notes

03 Communication and periodicity (notes see above).

04 Recurrence and transience, absorption. Notes

05 Absorption, ergodicity. Notes

06 Ergodicity, reversibility. Notes

07 Markov Chain Monte Carlo, Metropolis-Hastings Algorithm. Notes

Part 2

The provided slides

01 Non-parametric tests SLIDES Notes

02 Normalization SLIDES Notes article upper_quantile

03 Kernel Density Estimation SLIDES Notes - chapter2

04 Nonparametric Regression SLIDES

05 Support Vector Machines SLIDES additional_notes

06 Model Evaluation SLIDES

07 Classification and Regression trees SLIDES

08 Review notes_annalisa Task3 Task4 notes_Anna

Homework 1

Homework 2

Homework 3

Homework 4

Homework 5

Homework 6

Homework 7 additional_notes

Homework 8 dataset

Homework 9 protein_data tumor_data

Homework 10 ehec_data

Homework 11 patient_data

Homework 12 data1 data2

Homework 13 patient_data ehec_data

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