Computational Biology of Infection Research

The research of the group focuses on the data-driven analysis of biological questions from infection research, as well as method development to solve prediction problems for large biological data sets.

Leader

Seminar: Statistical Learning with Application in R

The seminar "Statistical Learning with Application in R" is hosted by the department "Computational Biology of Infection Research" at the HZI headed by Prof. Alice McHardy.

Kick-off Meeting: 21st of March 2017, at 4 pm s.t

Room Kick-off Meeting: Room 045 (BRICS)

Date: one day at the end of the semester break

Room seminar: tba

Max. number of participants: 9

Language: English

Modus: 30 minutes of presentation + 10 minutes of discussion + 3-5 pages summary Designated for Master Students of Computer Science

In case you have questions about the seminar, feel free to contact Thorsten Klingen.

Description:  This course is designed to teach the basic principles of statistical learning. We will discuss statistical learning techniques that are commonly used in bioinformatics and computer science for data clustering, classification and analysis. These techniques are applied to solve biological problems, e.g. in genomics, systems biology, and evolution and are useful to draw clear insights from your data. Further, the students can test statistical methods using basic commands in R, which is a powerful software environment for statistical computing, analysis and graphics.

Statistical Learning

1)    Introduction to Statistical Learning
2)    Linear Regression
3)    Classification
4)    Resampling Methods
5)    Linear Model Selection
6)    Non-linear Modeling
7)    Tree-Based Methods
8)    Support Vector Machines
9)    Unsupervised Learning

Literature:

“An Introduction to Statistical Learning with Application in R” by James, Witten, Hastie and Tibshirani

”The Elements in Statistical Learning“ by Friedman, Tibshirani, and Hastie

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