Machine Learning for Graphs and Sequential Data (IN2323)
Course 0000002444 in SS 2023
General Data
| Course Type | lecture with integrated exercises |
|---|---|
| Semester Weekly Hours | 4 SWS |
| Organisational Unit | Informatics 26 - Associate Professorship of Data Analytics and Machine Learning (Prof. Günnemann) |
| Lecturers |
Dominik Fuchsgruber Stephan Günnemann Yan Scholten Jan Schuchardt Johanna Sommer |
| Dates |
Wed, 14:00–16:00, Interims I 101 Thu, 14:00–16:00, Interims I 101 Wed, 14:00–16:00, online |
Assignment to Modules
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IN2323: Machine Learning for Graphs and Sequential Data / Machine Learning for Graphs and Sequential Data
This module is included in the following catalogs:- Focus Area Imaging in M.Sc. Biomedical Engineering and Medical Physics
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
| additional remarks | 1. Introduction * Machine Learning, Data Mining Process * Basic Terminology 2. Sequential Data * ML models for text data and temporal data * Autoregressive Models * HMMs, Kalman Filter * Embeddings (e.g. Word2Vec) * Neural Networks (e.g. RNN, LSTM) * Temporal Point Processes 3. Graphs & Networks * Laws, Patterns * (Deep) Generative Models for Graphs * Spectral Methods * Ranking (e.g., PageRank, HITS) * Community Detection * Node/Graph Classification * Label Propagation * Graph Neural Networks * (Unsupervised) Node Embeddings * Dynamic/temporal graphs |
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| Links |
Course documents E-Learning course (e. g. Moodle) Additional information TUMonline entry |