Teaching material and exercises
The lectures from earlier years are placed on this page to give the student an impression of what is coming. The lectures will be updated as the date approaches
Week 1
Lecture 1: Dynamic High Throughput Data in Biology (12.10.09)[PPT]
Lecture 2: Statistical analysis of Dynamic High Throughput Data (13.10.09) [PPT]
Lecture 3: Network Algorithms – Paths (19.10.09)
Lecture 4: Network Algorithms – Connectivity (20.10.09)
Lecture 5: Network Algorithms – Flow (26.10.09)
Lecture 6: Combinatorial Methods of Network Comparison (27.10.09)
Lecture 7: Probability Theory of Networks (2.11.09)
Lecture 8: Reconstruction Problems in Networks (3.11.09)
Lecture 9: Enumeration of Networks (9.11.09)
Lecture 10: Sampling Networks (10.11.09)
Week 6
Lecture 11: Network Inference (16.11.09)
Lecture 12: Network Robustness (17.11.09)
Reading material: A boosting approach to structure learning of graphs with and without prior knowledge
Modelling transcriptional regulation using Gaussian processes (not part of course material, but included as reference for the keen student)
Gaussian Processes for Machine Learning
Graphs, Networks, and Algorithms (only chapter 12 used in course)
Week 7
Lecture 13: Evolution of Networks I (23.11.09) [PPT]
Lecture 14: Evolution of Networks II (24.11.09) [PPT]
Week 8
Lecture 15: Network Inference II (30.11.09) [PPT]
Lecture 16: Computational Biology of Networks (1.12.09) [PPT]
Printer Friendly Version 