Inhalt

[ 921PECOMLPU20 ] UE Machine Learning and Pattern Classification

Versionsauswahl
(*) Unfortunately this information is not available in english.
Workload Education level Study areas Responsible person Hours per week Coordinating university
1,5 ECTS M1 - Master's programme 1. year Computer Science Gerhard Widmer 1 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Computer Science 2021S
Objectives Students will learn, in a practical, exploratory way, what it means to apply standard machine learning algorithms to real-world problems, by experimenting with learning and classification algorithms in a non-trivial application task. This will give them a critical understanding of the complexity of real-world learning, the methodology of systematic experimentation, critical evaluation of results, and possible pitfalls.
Subject Using public machine learning toolboxes, students will go through all the stages of a pattern classification project of real-world complexity, from annotation, feature definition and extraction to the training of various classifiers and systematic experimentation.
Criteria for evaluation Several written project reports throughout the course of the project.
Methods Independen work by students / student groups. Joint discussion of practical work. Presentations by lecturer and selected students or student groups.
Language English
Study material Will be provided if needed.
Changing subject? No
Corresponding lecture (*)in collaboration with 921PECOMLPV20: VL Machine Learning and Pattern Classification (3 ECTS) equivalent to
921PECOMLPK13: KV Machine Learning and Pattern Classification (4.5 ECTS)
On-site course
Maximum number of participants 35
Assignment procedure Direct assignment