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Machine Learning for Graphs and Sequential Data (IN2323)

Course 0000002444 in SS 2020

General Data

Course Type lecture with integrated exercises
Semester Weekly Hours 4 SWS
Organisational Unit Informatics 3 - Chair of database systems (Prof. Kemper)
Lecturers Bertrand Charpentier
Stephan Günnemann
Marten Lienen
Oleksandr Shchur
Daniel Zügner
Dates Wed, 14:00–16:00, virtuell
Thu, 14:00–16:00, virtuell

Assignment to Modules

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 * Introduction - Machine Learning, Data Mining Process - Basic Terminology * Scalability - Similarity Estimation - Filter-Refine Paradigm - Hashing & Sketches - Min-Hashing - Locality Sensitive Hashing - Membership Test / Bloom Filter - Large-Scale Optimization * Temporal Data & Sequences - Autoregressive Models - HMMs - Embeddings (e.g. Word2Vec) - Neural Networks (e.g. RNN, LSTM) * Graphs & Networks - Laws, Patterns - (Deep) Generative Models - VAE, Implicit Models - Generative Models for Graphs - Spectral Methods - Ranking (e.g., PageRank, HITS) - Community Detection - Representation Learning for Graphs - Graph Neural Networks - (Unsupervised) Node Embeddings
Links Course documents
E-Learning course (e. g. Moodle)
TUMonline entry

Equivalent Courses (e. g. in other semesters)

SS 2019 Mining Massive Datasets (IN2323) Günnemann, S.
Assistants: Bojchevski, A.Shchur, O.
Thu, 14:00–16:00, Interims I 101
Wed, 14:00–16:00, Interims I 101
SS 2018 Mining Massive Datasets (IN2323) Günnemann, S.
Assistants: Bojchevski, A.Shchur, O.
Wed, 14:30–16:00, Interims I 101
Thu, 14:00–16:00, Interims I 101
and singular or moved dates
WS 2016/7 Mining Massive Datasets (IN2323) Bojchevski, A. Günnemann, S.
WS 2015/6 Mining Massive Datasets (IN2323)
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