Mining Massive Datasets (IN2323)
Course 0000002444 in SS 2018
General Data
| Course Type | lecture with integrated exercises |
|---|---|
| Semester Weekly Hours | 4 SWS |
| Organisational Unit | Informatics 3 - Chair of database systems (Prof. Kemper) |
| Lecturers |
Stephan Günnemann Assistants: Oleksandr Shchur |
| Dates |
Wed, 14:30–16:00, Interims I 101 Thu, 14:00–16:00, Interims I 101 and 4 singular or moved dates |
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 and Knowledge Discovery Process * Applications, Tasks 2. High-Dimensional Data * Hashing & Sketches - Min-Hashing - Locality Sensitive Hashing * Dimensionality Reduction & Matrix Factorization - Feature Selection & Random Projections - Non-Negative Matrix Factorization and Extensions 3. Graphs / Networks * Laws, Patterns and Generators * Spectral Learning - Ranking (e.g., PageRank, HITS) - Community Detection * Probabilistic Models - Stochastic Blockmodel (SBM) - (Stochastic) Variational Inference - Belief Propagation * Representation Learning for Graphs - Deep Learning for Graph Data - (Unsupervised) Node Embeddings 4. Temporal Data & Streaming * Sampling & Sketches - Bloom Filter - Counting Distinct Elements - Estimating moments * Kalman Filter |
|---|---|
| Links |
Course documents E-Learning course (e. g. Moodle) TUMonline entry |