Life Science Data Analytics
and Algorithmic Bioinformatics
Teaching

Lecture: Data Mining and Machine Learning in Bioinformatics (SS 2017)

Location: B-IT

Time: 

Exercises (obligatory):

Prerequisites: Bachelor Computer Science or equivalent qualification

Content: This lecture gives a broad overview about frequently used statistical techniques as well as data mining and machine learning algorithms. The use of the respective methods to solve problems in bioinformatics is explained. The goal is to understand the explained methods, being able to apply them correctly and partially implement them. More detailed, the  the following topics are covered:

  • Introduction
  • Statistical Basics
    • Probability distributions and Bayesian inference
    • Statistical hypothesis testing
    • Linear models
    • Logistic regression
    • Principal Component Analysis
  • Data Mining and Machine Learning Algorithms
    • Clustering
    • Hidden Markov Models
    • Principles of Supervised Machine Learning
    • Elastic Net
    • Basics of deep learning

Prerequisites for getting credits are:

  • regular attendance of tutorials (missing at most 2 times allowed without medical certificate)
  • at least one group presentation of homework solutions
  • at least N-1 homeworks submitted
  • at least N-1 programming tasks submitted
  • >= 50% of points from all homework assignments
  • passing of exam

Literature:

  • N. Jones, P. Pevzner, An Introduction to Bioinformatics Algorithms, MIT Press, 2004
  • T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Springer, 2008
  • S.Boslaugh, P. Watters, Statistics in a Nutshell, O'Reilly, 2008

Seminar: Machine Learning Methods in the Life Sciences (SS 2018)

Location: U.105, b-it

Time: Mo, 9:00 - 10:30

Topics and presenters