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[ 675MLDAMSTV13 ] VL (*)Machine Learning: Supervised Techniques

Versionsauswahl
Es ist eine neuere Version 2015W dieser LV im Curriculum Bachelorstudium Bioinformatics 2015W vorhanden.
(*) Leider ist diese Information in Deutsch nicht verfügbar.
Workload Ausbildungslevel Studienfachbereich VerantwortlicheR Semesterstunden Anbietende Uni
3 ECTS B2 - Bachelor 2. Jahr Informatik Ulrich Bodenhofer 2 SSt Johannes Kepler Universität Linz
Detailinformationen
Quellcurriculum Bachelorstudium Bioinformatics 2013W
Ziele (*)Machine learning is concerned inferring models/relationships by learning from data. Machine learning methods are gaining importance in various fields, such as, process modeling, speech and image processing, and so forth. In recent years, bioinformatics has become one of the most prominent application areas of machine learning methods: The massive data amounts produced by recent and currently emerging high-throughput biotechnologies provide unprecedented potentials, but also pose yet unseen computational challenges in the analysis of biological data.

This course focuses on so-called supervised machine learning techniques, that is, methods aiming at models that classify data (classification) or predict continuous targets from inputs (regression). The students should acquire skills to choose, use, and adapt methods for classification, regression, and feature selection for tasks in science and engineering. The students should particularly understand the underlying mathematical objectives and principles of supervised machine learning methods. Furthermore, the students should be able to evaluate the results of supervised machine learning techniques.

Lehrinhalte (*)
  • Basics of classification and regression
  • Evaluation of machine learning results (confusion matrices, ROC)
  • Under- and overfitting / bias and variance
  • Cross-validation and hyperparameter selection
  • Logistic regression
  • Support vector machines and kernels
  • Neural networks and deep networks
  • Time series (sequence) analysis
  • Bagging and boosting
  • Feature selection and feature construction
Beurteilungskriterien (*)Exam (written or oral)
Lehrmethoden (*)Slide presentations complemented by examples presented on the blackboard
Abhaltungssprache Englisch
Literatur (*)Electronic course material is made available for download
Lehrinhalte wechselnd? Nein
Äquivalenzen (*)in collaboration with 875BIN2MUTV13: VL Machine Learning: Unsupervised Techniques (3 ECTS) equivalent to
875BIN2TMLV12: VL Theoretical Bioinformatics and Machine Learning (6 ECTS) -or- BIMPHVOBIN2: VO Bioinformatik II: Theoretische Bioinformatik und Maschinelles Lernen (6 ECTS)
Präsenzlehrveranstaltung
Teilungsziffer -
Zuteilungsverfahren Direktzuteilung