General information
| Course name | Vorlesung mit Übung: M.Inf.2103 Statistical Network Inference and Analysis |
| Subtitle | |
| Course number | 440917 |
| Semester | SoSe 2024 |
| Current number of participants | 11 |
| maximum number of participants | 25 |
| Home institute | Institut für Medizinische Bioinformatik (UMG) |
| Courses type | Vorlesung mit Übung in category Teaching |
| First date | Tuesday, 09.04.2024 11:30 - 13:00, Room: (2.122 (GZG (Goldschmidtstr. 1))) |
| Type/Form | |
| Pre-requisites | Basic knowledge about statistical learning |
| Learning organisation | • Learn the concepts of different network inference methods for observational data, such as probabilistic graphical models, e.g., Gaussian and Mixed Graphical Models or the Markov Random Field • Gain a solid understanding about regularization strategies to deal with large feature spaces, e.g., graphical lasso and covariance shrinkage • Learn state-of-the-art optimization strategies and use them to the implement networks inference methods • Acquire practical experience in network inference using diverse data types, e.g., demographic or biomedical data • Understand the concept of Directed Acyclic Graphs (DAGs) and learn to estimate lower bounds for causal effects from observational data • Understand and apply network inference methods for time-course data • Understand and apply analysis strategies for networks, e.g., community detection methods |
| Performance record |
→Ab hier automatisch erfasste Informationen / Beyond this point, the information is filled in automatically← Prüfungsleistung(en) je Modul / Exam details per module: * [(M.Inf.2103.Mp) Statistical Network Inference and Analysis][1] * Mündlich: Mo, 30.09.2024, von 10:00:00 bis 12:00:00 [1]: https://ecampus.uni-goettingen.de/h1/pages/startFlow.xhtml?_flowId=detailView-flow&unitId=53801&periodId=272 |
| Miscellanea | Hastie, et al. Elements of Statistical Learning https://web.stanford.edu/~hastie/ElemStatLearn/ |
