0.25
0.5
0.75
1.25
1.5
1.75
2
Learning Models, Supermodels, and Ensemble Models of Dynamic Systems
Published on Apr 24, 20142063 Views
Related categories
Chapter list
Learning Models, Supermodels, and Ensemble Models of Dynamic Systems00:00
Learning Models of Dynamic Systems00:12
Learning Models of Dynamic Systems: An Example00:37
Learning Models of Dynamic Systems: A Real-world Example from Ecology01:14
A Machine Learning Approach to Learning Models of Dynamic Systems - 101:53
A Machine Learning Approach to Learning Models of Dynamic Systems - 202:41
Using Domain Knowledge04:43
Data and Knowledge-Driven Modeling06:20
Process Models (PM)08:02
PM: Qualitative Aspect09:44
PM: Quantitative Aspect10:27
Inductive Process Modelling11:00
Libraries of Domain Knowledge12:51
Hierarchies of Species and Processes14:07
Alternative Formulations14:35
Modeling Task Specification15:22
IPM: Searching for Process Models16:50
IPM: Generate Models - 117:36
IPM: Generate Models - 218:51
ProBMoT: A SW Platform for IPM19:13
Parameter Estimation in ProBMoT20:18
Applications of IPM21:05
Modeling Aquatic Ecosystems22:25
Automated Modeling of Lake EcoSystems22:46
Applications in Systems Biology/ ‘Reconstructing’ Biological Networks23:09
‘Reconstructing’ Biological Networks24:59
Modeling Knowledge for Metabolic Networks25:42
Example Application: Glycolisys26:50
Induced Glycolysis Network27:05
Systems vs. Synthetic Biology27:27
ProBMoT for Synthetic Biology28:18
Case Study: Biochemical Adaptation - 129:43
Case Study: Biochemical Adaptation - 230:26
Design of Biological Circuits with Complex Behaviours31:50
Case Study: Repressilator32:34
Case Study: Coupled Repressilators32:39
Summary I32:45
Model Ensembles and Supermodels33:12
Learning Lorenz Models and Supermodels34:50
Lorenz: A Library for Process-based Modeling35:52
Lorenz: An Example Process-based Model36:32
A Process-based Library for Supermodeling37:08
An Example Supermodel: Components38:18
An Example Supermodel: Couplings39:01
Learning a Component Model from Data39:44
Learning Couplings in a Supermodel40:21
Ensemble Models in Machine Learning40:44
Using Ensemble of Models42:06
Constructing an Ensemble of Models42:22
Constructing of Different Training Sets: Bagging42:28
(Bootstrap) Sampling from Time Series43:08
Learning Ensemble Models with ProBMoT44:05
Summary II44:49
Thank you!45:09
DS-2014: 17th International Conference on Discovery Science45:38