[ 521ANWEIMLV18 ] VL Introduction to Machine Learning

(*) Unfortunately this information is not available in english.
Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS B3 - Bachelor's programme 3. year Computer Science G√ľnter Klambauer 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Bachelor's programme Computer Science 2021S
Objectives Students understand and master essential techniques of machine learning.
Subject Machine learning is concerned inferring models/relationships by learning from data. Machine learning methods have become indispensable in various fields, such as, process modeling, computer vision, signal processing, speech and language processing, life sciences, and so forth. This course features the most essential concepts of machine learning and gives an overview of the most important methods. The methodological subjects are complemented by examples of exciting recent real-world applications of machine learning methods.

  • Taxonomy of machine learning: supervised vs. unsupervised learning, reinforcement learning, classification vs. regression;
  • Examples of basic methods: nearest neighbor, linear regression, k-means, principal component analysis
  • Basics of evaluating machine learning models: confusion tables, ROC curves
  • Support vector machines and random forests (+ examples from life sciences)
  • Neural networks and Deep Learning (+ examples from image analysis, drug design, and language processing)
  • Clustering and biclustering
Criteria for evaluation Written exam
Methods Slide presentations complemented by online demos and examples presented on the blackboard
Language English and French
Study material Electronic course material is made available for download
Changing subject? No
Corresponding lecture (*)INBIPVOBIIN: VO Bioinformatics (3 ECTS)
On-site course
Maximum number of participants -
Assignment procedure Direct assignment