Machine learning for mobile and pervasive systems
Form of learning:
Target group and prerequisites
During this course, students will learn good practices for machine learning with noisy and inaccurate data.
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.
Lectures on Wednesdays & Fridays at 14–16.
Examination, assignments and group works.
Application deadline: 4.1.2022.