INF.07654.02 - Statistical Data Analysis and Machine Learning in Biodiversity Research (Complete module description)

INF.07654.02 - Statistical Data Analysis and Machine Learning in Biodiversity Research (Complete module description)

Original version English
INF.07654.02 5 CP
Module label Statistical Data Analysis and Machine Learning in Biodiversity Research
Module code INF.07654.02
Semester of first implementation
Faculty/Institute Institut für Informatik
Module used in courses of study / semesters
  • Biodiversity Sciences (MA120 LP) (Master) > Biologie BiodiversityMA120, Version of accreditation valid from SS 2021 > Project modules offered by the Institute of Computer Science (Nat Sci III)
Responsible person for this module
Further responsible persons
Prof. Dr. Ivo Große
Prerequisites
Skills to be acquired in this module
  • Acquire an understanding of the fundamental principles and techniques of statistical data analysis and machine learning in bioinformatics and biodiversity research.
  • Being capable to critically evaluate and select appropriate statistical data analysis and machine learning approaches for addressing specific scientific problems in bioinformatics and biodiversity research.
  • Gaining the ability to effectively communicate and explain statistical data analysis and machine learning approaches to both technical and non-technical audiences including their limitations and implications of their findings.
  • Having competence of developing and advancing these approaches further for application to new and emerging scientific challenges in bioinformatics and biodiversity research.
Module contents
  • Statistical inference; ML, MAP, MP estimators; bias, variance, consistency, efficiency of estimators
  • Markov models; ML, MAP, MP estimators; sequence logos; applications in bioinformatics and biodiversity research
  • Hidden Markov models; Viterbi algorithm, forward-backward algorithm; applications in bioinformatics and biodiversity research
  • Model selection; model averaging; classification; applications in bioinformatics and biodiversity research
Forms of instruction Lecture (2 SWS)
Course
Exercises (2 SWS)
Exercises
Languages of instruction German, English
Duration (semesters) 1 Semester Semester
Module frequency jedes Sommersemester
Module capacity unrestricted
Time of examination
Credit points 5 CP
Share on module final degree Course 1: %; Course 2: %; Course 3: %; Course 4: %.
Share of module grade on the course of study's final grade 1
Module course label Course type Course title SWS Workload of compulsory attendance Workload of preparation / homework etc Workload of independent learning Workload (examination and preparation) Sum workload
Course 1 Lecture Vorlesung 2 0
Course 2 Course Selbststudium 0
Course 3 Exercises Übung 2 0
Course 4 Exercises Bearbeiten der Übungsaufgaben 0
Workload by module 150 150
Total module workload 150
Examination Exam prerequisites Type of examination
Course 1
Course 2
Course 3
Course 4
Final exam of module
Aktive Teilnahme an den Übungen, Erfolgreiches Lösen der Übungs- und Programmieraufgaben, Erfolgreiches Vorrechnen und Erklären der Lösungen, 50% der Punkte der Übungsaufgaben
mündliche Prüfung oder Klausur
Exam repetition information
Prerequisites and conditions Prerequisites Frequency Compulsory attendance Share on module grade in percent
Course 1 Summer semester No %
Course 2 Summer semester No %
Course 3 Summer semester No %
Course 4 Summer semester No %