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Machine learning for environmental and life sciences
Published on Mar 27, 2019256 Views
Increasingly often, we need to learn predictive models from big or complex data, which may comprise many examples and many input/output dimensions. When more than one target variable has to be predict
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Chapter list
Machine Learning for Environmental & Life Sciences00:00
What is artificial intelligence?00:37
Machine Learning01:45
Data Science02:50
The most popular data science topic04:53
Machine Learning of Interpretable Models06:33
The basic Machine Learning task: Predictive modeling06:53
An example task of Predictive Modelling: Medical diagnosis07:20
Example task: Descriptive vars.; Biomarkers for Alzheimer’s07:48
Example: Decision tree for diagnosis08:14
Another example of single-target predictive modeling (classification) - 108:28
Another example of single-target predictive modeling (classification) - 209:11
What is a decision tree?09:39
Making a Prediction with a Decision Tree10:22
Machine Learning of Decision Trees11:45
Top-Down Induction of Decision Trees15:02
TDIDT Illustrated15:06
Mining Big and Complex Data:Dimensions of Complexity17:01
Mining Big and Complex Data17:45
Big Data: Variety - Structured Input - 119:07
Big Data: Variety - Structured Input - 219:46
Predictive modeling: Structured output20:16
Big Data: Volume & Velocity20:37
Data streams: Regression21:11
Semi-supervised learning: Classification and regression21:32
Data in context: Spatio-temporal, network22:59
The Different Tasks of Multi-Target Prediction23:54
Weather prediction24:11
Multi-target prediction25:04
Example MTR task: Target vars.; Clinical scores for Alzheimer’s25:31
Example MTR model26:23
Multi-Target Classification & Multi-Label Classification27:03
Multi-Label Classification Example27:40
Hierarchical multi-label classification28:24
Hierarchical multi-label classif.28:36
Hierarchical multi-label classification: Another example30:00
The hierarchy can be a tree or a DAG30:21
Hierarchical structure on target space for the ADNI dataset30:51
Mining Big and Complex Data:Combining Complexities31:37
Data streams: (MT) Regression32:08
Network +SOP: HMC32:16
Even more complicated tasks33:38
Predictive Clustering for Multi-Target Prediction33:53
Clustering34:29
Example predictive clustering tree35:17
Top-down induction of PCTs36:47
Predictive clustering38:51
Selecting the best test in a PCT39:42
Multi-target regression39:52
Multi-target classification40:02
Ensembles of PCTs40:18
SSL+SOP: Incomplete Annotations41:20
Learning PCTs41:36
Relating the Environment and the Biota41:50
Environment <-> Biota42:46
Habitat modeling43:04
Predicting species composition43:12
Predicting community structure43:19
Slovenian rivers43:28
Danish farms: Soil Microarthropods43:58
Victoria, AustraliaVegetation44:03
Slovenian rivers: Species comp.44:30
Slovenian rivers: Habitat models44:56
Slovenian rivers: Community struc.45:10
Community structure: Overall results45:26
Predicting Gene Functions48:50
Predicting gene functions49:01
Predicting Gene Functions in Bacterial Genomes (RBI+JSI)49:34
GFP Pipeline50:37
Different Features Sets for GFP51:27
Gene Function Prediction: Predictive Performance52:03
Metagenome Phyletic Profiles52:50
Multi-Target Prediction for Virtual Compound Screening54:09
Virtual compound screening54:25
Host-targeted Drugs for MTB (Tuberculosis) and STM (Salmonella)56:20
MTB&STM: Host-targeted Drugs57:06
MTB&STM: Host-targeted Drugs The Data Analysis Workflow57:43
MTB&STM: Host-targeted DrugsResults58:40
Analyzing data from High-contents Screens58:58
HTS: Modulating fibroblast to myofibroblast transition59:33
Hits in the HTS screen59:43
Reducing fibrosis in myocardial infarction59:53
Testing the predictions01:00:07
Spring school in Bled in May01:00:51
Conclusions01:01:37