This lecture promotes understanding of good practices for machine learning with noisy and
In particular, we focus on feature extraction/ feature subset selection, handling high dimensional data, ANN + Deep Learning, Probabilistic graphical models, Topic models; as well as Unsupervised learning and clustering, Anomaly detection and Recommender systems.
The lecture exercises are project-based and foster hands-on experience with machine learning approaches on relevant data sets.
The lecture schedule is given below:
The course is organized in a modular design. Students are free to select any combination of modules. Credits are granted according to the completion of modules. Grading is possible with some modules.
- Lectures (1 cr): Physical attendance and active participation in at least 10 contact teaching sessions
- Video lectures (0 cr): Video lectures are provided for some topics to further the learning of the topic.
- Tutorials (1cr, graded): Preparation, training and presentation in front of the class of an in-depth tutorial on a specialized theme
- Projects (2cr, graded): Guided Project work in teams of 3 students on a research or industry-relevant topic/data. Video-status reports throughout the project duration and final presentation in form of a poster.
- Report (2cr, graded, precondition: project): Written technical report on the project work and outcomes. In case of novel results, submission to a scientific venue can be supported.
- Oral exam (1cr, graded): Individual technical discussion (20 minutes) on the topics taught in the class
- Practical training sessions (0cr): Contact session and online resources to practice common ML tools
- Ulf Kulau, DSI Aerospace Technology (10.01.2020)
- Jere Nieminen, Elisa Viihde (15.01.2020)
- Thomas Jochmann, TU Ilmenau (05.02.2020)