Inhalt

[ 675MLDAMSTV13 ] VL Machine Learning: Supervised Techniques

Versionsauswahl
Es ist eine neuere Version 2015W dieser LV im Curriculum Bachelor's programme Bioinformatics 2015W vorhanden.
Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS B2 - Bachelor's programme 2. year Computer Science Ulrich Bodenhofer 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Bachelor's programme Bioinformatics 2013W
Objectives 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.

Subject
  • 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
Criteria for evaluation Exam (written or oral)
Methods Slide presentations complemented by examples presented on the blackboard
Language English
Study material Electronic course material is made available for download
Changing subject? No
Corresponding lecture 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)
On-site course
Maximum number of participants -
Assignment procedure Direct assignment