Mining Massive Datasets
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of SS 2015
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
|available module versions|
|WS 2019/20||SS 2015|
IN2323 is a semester module in English language at Master’s level which is offered in summer semester.
This module description is valid to SS 2019.
|Total workload||Contact hours||Credits (ECTS)|
|150 h||60 h||5 CP|
Content, Learning Outcome and Preconditions
* Machine Learning, Data Mining and Knowledge Discovery Process
* Applications, Tasks
2. High-Dimensional Data
* Hashing & Sketches
- 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 Graph Theory
- 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
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VI||4||Machine Learning for Graphs and Sequential Data (IN2323)||Fuchsgruber, D. Günnemann, S. Scholten, Y. Schuchardt, J. Sommer, J.||
Wed, 14:00–16:00, Interims I 101
Thu, 14:00–16:00, Interims I 101
Learning and Teaching Methods
- Data Mining: The Textbook. Charu Aggarwal. Springer. 2015
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Trevor Hastie, Robert Tibshirani, Jerome Friedman. Springer. 2011
Description of exams and course work
The exam may be repeated at the end of the semester.