siebert@mi.fu-berlin.de

Arnimallee 7, Raum 103

Office hours: by email appointment Bernhard Renard

RenardB@rki.de

RKI, Nordufer 20, Room N01.O2.014

Office hours: by email appointment Jakob Schulze

jakob.schulze@fu-berlin.de

The repeat exam will be held at

Here are the results of the final exam.

An R course is offered in an FU qualifying programm. More information here. There are also many free online courses such as this one. We also provide a small tutorial as exercise 0. Please also use the opportunity to ask in the exercises for help before the first real R problem is posted.

You can get extra credit for the exercises (if you have not received >50% of the points on the reviews) by handing in your solution to problem set 15.

SWS: 2

Exercises:

SWS: 2

ECTS: 6

Language: English

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

First lecture 20.10.

Exercises:

Monday, 12-14h in SR 032 (Arnimallee 6), 14-16h, SR 025/026 (Arnimallee 6)

First exercises 24.10.

Computational Statistics and Statistical Learning

Attending the lecture is highly recommended. A 90 minute

Both for the reviews and the exam no tools (script, calculator…) besides a pen are allowed. However, you are allowed to have a single page (DINA4, front side only) of hand written notes at the exam. Please bring a student and a photo ID.

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 (notes)

06 Ergodicity, Reversibility (notes)

07 Markov Chain Monte Carlo (notes)

08 MCMC II (notes see above) Part 2

The provided slides

01 Introduction (slides)

02 Non-parametrics (slides) (reading material)

03 Kernel Density Estimation (slides) (reading material)

04 Kernel Regression (slides) (reading material)

05 Model Evaluation (slides) (reading material)

06 Support Vector Machines (slides) (reading material (German)) (alternative reading material (Chapters 4.3, 12.1-12.3))

07 Classification Trees (slides) (reading material)

08 Bagging and Random Forests (slides) (reading material)

09 Boosting (slides) (reading material)

10 Normalization (slides) (reading material)

Problem sheet 1

Problem sheet 2

Problem sheet 3

Problem sheet 4

Problem sheet 5

Problem sheet 6

Problem sheet 7

Problem sheet 8

Problem sheet 9

Problem sheet 10 data

Problem sheet 11 data

Problem sheet 12 data data

Problem sheet 13 data

Problem sheet 14 data

Problem sheet 15 data

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