Kursinformation

20-00-1118-iv

Course Contents:
Background and concepts of human-centric Machine Learning: the goal of identity and
human-centric machine learning. The differences between identity learning and other
mainstream classification.
Representation extraction for subject-related data: feature extraction methodology for
identity related applications. Hand crafted and Deeply learned features background and
basics.
Deep learning strategies for identity representations: learning identities representations with
deep learning. Learning strategies and learning losses. Network architectures and identityspecific components.
Knowledge transfer and distillation: transfer learning and identity-representation. Knowledge
distillation concepts and applications.
Efficient machine learning: the relation between resource limitations, Green-AI, and deep
learning. Methods to build efficient machine learning solutions.
Synthetic identity: the need of synthetic identity. Synthetic identity as adversarial.
Generating synthetic identity-controlled data under different restrictions.
Machine learning biases: analyses of demographic fairness and the roots of the fairness
issues. ML-based mitigation of demographic biases.
Learning privacy: analyzing unintentionally learned information. Learning strategies to the
targeted suppression of information at different representation levels.
Data utility: understanding the effect of data utility in the training process. Understanding
sample utility in operation. ML concepts and strategies of estimating sample utilities.
Sample-level attacks: overview on adversarial, sample manipulation, other attacks on
human-centric ML. Deep learning concepts, network blocks, and loss strategies, to detect
and mitigate sample-level attacks.
Explainability: overview on the need for explainability in different decision-making processes.
Different strategies to provide explainability for decision made in different operations
discussed in the previous lectures.
Ethics in identity-centric ML: overview on ethics in AI and AI regulation. AI ethics for human
data processing and storage.