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This commit is contained in:
Simon Klüttermann
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<frame >
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<titlepage>
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</frame>
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<frame title="Anomaly Detection">
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<split>
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<que>
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<list>
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<e>Two distributions</e>
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<l2st>
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<e>One known (=normal)</e>
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<e>One unknown (=anomalies)</e>
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||||||
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</l2st>
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<e>Seperate them</e>
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</list>
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</que>
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||||||
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<que>
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||||||
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<i f="..//prep/01Anomaly_Detection/anomalies.pdf" wmode="True"></i>
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</que>
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</split>
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</frame>
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<frame title="Anomaly Detection">
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<split>
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||||||
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<que>
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||||||
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<list>
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||||||
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<e>Two distributions</e>
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||||||
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<l2st>
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||||||
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<e>One known (=normal)</e>
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<e>One unknown (=anomalies)</e>
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</l2st>
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<e>Seperate them</e>
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<e>Problem: few anomalies</e>
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</list>
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||||||
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</que>
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<que>
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<i f="..//prep/02Anomaly_Detection/difference.pdf" wmode="True"></i>
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</que>
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</split>
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</frame>
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<frame title="Anomaly Detection">
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<split>
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<que>
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<list>
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<e>Anomalies are rare, so often only a few datapoints known (e.g. Machine Failure in an Aircraft)</e>
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<e>In practice, anomalies might appear that are not known during testing</e>
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<e>->So train the model only on normal samples</e>
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<e>Unsupervised Machine Learning</e>
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<l2st>
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<e>What can we say without knowing anomalies?</e>
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<e>''Understand you dataset''</e>
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||||||
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</l2st>
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</list>
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||||||
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</que>
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||||||
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<que>
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||||||
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<i f="..//prep/03Anomaly_Detection/usup.pdf" wmode="True"></i>
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</que>
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||||||
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</split>
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</frame>
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@ -0,0 +1,19 @@
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||||||
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<frame title="Anomaly Detection">
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||||||
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<split>
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||||||
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<que>
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||||||
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<list>
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||||||
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<e>Anomalies are rare, so often only a few datapoints known (e.g. Machine Failure in an Aircraft)</e>
|
||||||
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<e>In practice, anomalies might appear that are not known during testing</e>
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||||||
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<e>->So train the model only on normal samples</e>
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||||||
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<e>Unsupervised Machine Learning</e>
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||||||
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<l2st>
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||||||
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<e>What can we say without knowing anomalies?</e>
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||||||
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<e>''Understand you dataset''</e>
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||||||
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</l2st>
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||||||
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</list>
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||||||
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</que>
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||||||
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<que>
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<i f="..//prep/04Anomaly_Detection/circle.pdf" wmode="True"></i>
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</que>
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</split>
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</frame>
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<frame title="Anomaly Detection">
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<split>
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<que>
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<list>
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||||||
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<e>Seems easy? Now do this</e>
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<l2st>
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<e>in thousands of dimensions</e>
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<e>with complicated distributions</e>
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<e>and overlap between anomalies and normal points</e>
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||||||
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</l2st>
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||||||
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</list>
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||||||
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</que>
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||||||
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<que>
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||||||
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<i f="..//prep/05Anomaly_Detection/anomaly_detection.png" wmode="True"></i>
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</que>
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</split>
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</frame>
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<frame title="AutoML">
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<split>
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<que>
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<list>
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||||||
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<e>Most machine learning requires Hyperparameter Optimisation</e>
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||||||
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<e>(Find model parameters that result in the best results)</e>
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||||||
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<e>->AutoML: Do this automatically as fast as possible</e>
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||||||
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</list>
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||||||
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</que>
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<que>
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||||||
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<i f="..//prep/06AutoML/Download.png" wmode="True"></i>
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</que>
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</split>
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</frame>
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<frame title="AutoAD">
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<list>
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||||||
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<e>So lets combine both (Auto Anomaly Detection)</e>
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<e>->Problem</e>
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||||||
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<l2st>
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||||||
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<e>AutoMl requires Evaluation (loss, accuracy, AUC) to optimize</e>
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<e>AD can only be evaluated with regards to the anomalies</e>
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<e>->no longer unsupervised</e>
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||||||
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</l2st>
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<e>So most Anomaly Detection is ''unoptimized''</e>
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</list>
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||||||
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</frame>
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@ -0,0 +1,14 @@
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<frame title="Solution 1 Metrics">
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<split>
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<que>
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||||||
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<list>
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||||||
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<e>So how to solve this?</e>
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<e>One option: Think of some function to evaluate only the normal points</e>
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<e>->A bit hard to do in a case study</e>
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</list>
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||||||
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</que>
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<que>
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||||||
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<i f="..//prep/08Solution_1_Metrics/circle2.pdf" wmode="True"></i>
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</que>
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</split>
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</frame>
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<frame title="Solution 2 OneShot Learning">
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||||||
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<list>
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||||||
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<e>So how to solve this?</e>
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||||||
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<e>One option: ''Just find the best solution directly''</e>
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<e>->Zero Shot AutoML</e>
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<e>Find best practices for hyperparameters</e>
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<e>Requires optimisation for each model seperately -> matches the case study structure quite well!</e>
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||||||
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</list>
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</frame>
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@ -0,0 +1,23 @@
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<frame title="Course">
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<split>
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<que>
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<list>
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||||||
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<e>Basics of Scientific Computing</e>
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<e>Basics of AD</e>
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||||||
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<e>Basics of AutoML</e>
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||||||
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<e>Build groups for each algorithm</e>
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||||||
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<l2st>
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<e>Choose a set of Hyperparameters</e>
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||||||
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<e>Find ''best practice`s'' for them</e>
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||||||
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<e>Maybe consider more complicated Transformations (Preprocessing, Ensemble)</e>
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||||||
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</l2st>
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||||||
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<e>Compare between groups (best algorithm for current situation)</e>
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||||||
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<e>Evaluate on new datasets</e>
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||||||
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<e>Write a report/Present your work</e>
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||||||
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</list>
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||||||
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</que>
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||||||
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<que>
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||||||
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<i f="..//prep/09Course/table.png" wmode="True"></i>
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</que>
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</split>
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</frame>
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@ -0,0 +1,10 @@
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<frame title="Questions">
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||||||
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<list>
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||||||
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<e>Requirements:</e>
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||||||
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<l2st>
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||||||
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<e>MD Req 1->MD Req 8</e>
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||||||
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<e>Basic Python/Math Knowledge</e>
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||||||
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<e>Motivation to learn something new;)</e>
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||||||
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</l2st>
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||||||
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</list>
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||||||
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</frame>
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<plt>
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<name Current experiment status>
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<title Case Study - AutoML for Robust Anomaly Detection>
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<stitle AutoML4Rad>
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<institute ls9 tu Dortmund>
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<theme CambridgeUS>
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<colo dolphin>
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</plt>
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@ -0,0 +1 @@
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Subproject commit 62ffd6ae589d7983791feea9d44d7658534d54a0
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pdflatex main.tex
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pdflatex main.tex
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pdflatex main.tex
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pdflatex main.tex
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[
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{
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"typ": "img",
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"files": [
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"..//prep/01Anomaly_Detection/anomalies.pdf"
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],
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"label": "prep01Anomaly_Detectionanomaliespdf",
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"caption": "",
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"where": "../case1/data/001Anomaly Detection.txt"
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},
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{
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"typ": "img",
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"files": [
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"..//prep/02Anomaly_Detection/difference.pdf"
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],
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"label": "prep02Anomaly_Detectiondifferencepdf",
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"caption": "",
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"where": "../case1/data/002Anomaly Detection.txt"
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},
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{
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"typ": "img",
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"files": [
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"..//prep/03Anomaly_Detection/usup.pdf"
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],
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"label": "prep03Anomaly_Detectionusuppdf",
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"caption": "",
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"where": "../case1/data/003Anomaly Detection.txt"
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},
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{
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"typ": "img",
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"files": [
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"..//prep/04Anomaly_Detection/circle.pdf"
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],
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"label": "prep04Anomaly_Detectioncirclepdf",
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"caption": "",
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"where": "../case1/data/004Anomaly Detection.txt"
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},
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{
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"typ": "img",
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"files": [
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"..//prep/05Anomaly_Detection/anomaly_detection.png"
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],
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"label": "prep05Anomaly_Detectionanomaly_detectionpng",
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"caption": "",
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"where": "../case1/data/005Anomaly Detection.txt"
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},
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{
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"typ": "img",
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"files": [
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"..//prep/06AutoML/Download.png"
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],
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"label": "prep06AutoMLDownloadpng",
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"caption": "",
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"where": "../case1/data/006AutoML.txt"
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},
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{
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"typ": "img",
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"files": [
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"..//prep/08Solution_1_Metrics/circle2.pdf"
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],
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"label": "prep08Solution_1_Metricscircle2pdf",
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"caption": "",
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"where": "../case1/data/008Solution 1 Metrics.txt"
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},
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{
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"typ": "img",
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"files": [
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"..//prep/09Course/table.png"
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],
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"label": "prep09Coursetablepng",
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"caption": "",
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"where": "../case1/data/010Course.txt"
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}
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]
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\relax
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\providecommand\hyper@newdestlabel[2]{}
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\providecommand\HyperFirstAtBeginDocument{\AtBeginDocument}
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\HyperFirstAtBeginDocument{\ifx\hyper@anchor\@undefined
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\global\let\oldcontentsline\contentsline
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\gdef\contentsline#1#2#3#4{\oldcontentsline{#1}{#2}{#3}}
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\global\let\oldnewlabel\newlabel
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\gdef\newlabel#1#2{\newlabelxx{#1}#2}
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\gdef\newlabelxx#1#2#3#4#5#6{\oldnewlabel{#1}{{#2}{#3}}}
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\AtEndDocument{\ifx\hyper@anchor\@undefined
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\let\contentsline\oldcontentsline
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\let\newlabel\oldnewlabel
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\fi}
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\fi}
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\global\let\hyper@last\relax
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\gdef\HyperFirstAtBeginDocument#1{#1}
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\providecommand\HyField@AuxAddToFields[1]{}
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\providecommand\HyField@AuxAddToCoFields[2]{}
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\@writefile{nav}{\headcommand {\slideentry {0}{0}{1}{1/1}{}{0}}}
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\@writefile{nav}{\headcommand {\beamer@framepages {1}{1}}}
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\newlabel{Anomaly Detection<1>}{{2}{2}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {Anomaly Detection<1>}{2}}
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\newlabel{Anomaly Detection}{{2}{2}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {Anomaly Detection}{2}}
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\newlabel{fig:prep01Anomaly_Detectionanomaliespdf}{{2}{2}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {fig:prep01Anomaly_Detectionanomaliespdf}{2}}
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\@writefile{nav}{\headcommand {\slideentry {0}{0}{2}{2/2}{}{0}}}
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\@writefile{nav}{\headcommand {\beamer@framepages {2}{2}}}
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\newlabel{Anomaly Detection<1>}{{3}{3}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {Anomaly Detection<1>}{3}}
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\newlabel{Anomaly Detection}{{3}{3}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {Anomaly Detection}{3}}
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\newlabel{fig:prep02Anomaly_Detectiondifferencepdf}{{3}{3}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {fig:prep02Anomaly_Detectiondifferencepdf}{3}}
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\@writefile{nav}{\headcommand {\slideentry {0}{0}{3}{3/3}{}{0}}}
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\@writefile{nav}{\headcommand {\beamer@framepages {3}{3}}}
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\newlabel{Anomaly Detection<1>}{{4}{4}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {Anomaly Detection<1>}{4}}
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\newlabel{Anomaly Detection}{{4}{4}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {Anomaly Detection}{4}}
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\newlabel{fig:prep03Anomaly_Detectionusuppdf}{{4}{4}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {fig:prep03Anomaly_Detectionusuppdf}{4}}
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\@writefile{nav}{\headcommand {\slideentry {0}{0}{4}{4/4}{}{0}}}
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\@writefile{nav}{\headcommand {\beamer@framepages {4}{4}}}
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\newlabel{Anomaly Detection<1>}{{5}{5}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {Anomaly Detection<1>}{5}}
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\newlabel{Anomaly Detection}{{5}{5}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {Anomaly Detection}{5}}
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\newlabel{fig:prep04Anomaly_Detectioncirclepdf}{{5}{5}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {fig:prep04Anomaly_Detectioncirclepdf}{5}}
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\@writefile{nav}{\headcommand {\slideentry {0}{0}{5}{5/5}{}{0}}}
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\@writefile{nav}{\headcommand {\beamer@framepages {5}{5}}}
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\newlabel{Anomaly Detection<1>}{{6}{6}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {Anomaly Detection<1>}{6}}
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\newlabel{Anomaly Detection}{{6}{6}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {Anomaly Detection}{6}}
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\newlabel{fig:prep05Anomaly_Detectionanomaly_detectionpng}{{6}{6}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {fig:prep05Anomaly_Detectionanomaly_detectionpng}{6}}
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\@writefile{nav}{\headcommand {\slideentry {0}{0}{6}{6/6}{}{0}}}
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\@writefile{nav}{\headcommand {\beamer@framepages {6}{6}}}
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\newlabel{AutoML<1>}{{7}{7}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {AutoML<1>}{7}}
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\newlabel{AutoML}{{7}{7}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {AutoML}{7}}
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\newlabel{fig:prep06AutoMLDownloadpng}{{7}{7}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {fig:prep06AutoMLDownloadpng}{7}}
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|
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|
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|
||||||
|
\title[AutoML4Rad]{Case Study - AutoML for Robust Anomaly Detection}
|
||||||
|
\author{Simon Kluettermann}
|
||||||
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|
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%from file ../case1/data/000.txt
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{\huge\bfseries \par}
|
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|
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{\LARGE\itshape Simon Kluettermann\par}
|
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{\scshape\Large Master Thesis in Physics\par}
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|
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|
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|
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|
{ \Large\par}
|
||||||
|
\vspace{0.2cm}
|
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|
{\Large First Referee: Prof. Dr. Michael Kraemer \par}
|
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|
{\Large Second Referee: Prof. Dr. Felix Kahlhoefer}
|
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{\large November 2020 \par}
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%from file ../case1/data/001Anomaly Detection.txt
|
||||||
|
\begin{frame}[label=Anomaly Detection]
|
||||||
|
\frametitle{Anomaly Detection}
|
||||||
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\begin{columns}[c] % align columns
|
||||||
|
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|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Two distributions
|
||||||
|
|
||||||
|
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|
||||||
|
|
||||||
|
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|
||||||
|
|
||||||
|
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|
||||||
|
|
||||||
|
|
||||||
|
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|
||||||
|
\item Seperate them
|
||||||
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||||||
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|
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|
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|
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|
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|
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|
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|
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|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{..//prep/01Anomaly_Detection/anomalies.pdf}
|
||||||
|
\label{fig:prep01Anomaly_Detectionanomaliespdf}
|
||||||
|
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|
||||||
|
|
||||||
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|
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|
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|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case1/data/002Anomaly Detection.txt
|
||||||
|
\begin{frame}[label=Anomaly Detection]
|
||||||
|
\frametitle{Anomaly Detection}
|
||||||
|
\begin{columns}[c] % align columns
|
||||||
|
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|
||||||
|
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|
||||||
|
|
||||||
|
\item Two distributions
|
||||||
|
|
||||||
|
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|
||||||
|
|
||||||
|
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|
||||||
|
|
||||||
|
\item One unknown (=anomalies)
|
||||||
|
|
||||||
|
|
||||||
|
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|
||||||
|
\item Seperate them
|
||||||
|
|
||||||
|
\item Problem: few anomalies
|
||||||
|
|
||||||
|
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{..//prep/02Anomaly_Detection/difference.pdf}
|
||||||
|
\label{fig:prep02Anomaly_Detectiondifferencepdf}
|
||||||
|
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|
||||||
|
|
||||||
|
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
|
||||||
|
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|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case1/data/003Anomaly Detection.txt
|
||||||
|
\begin{frame}[label=Anomaly Detection]
|
||||||
|
\frametitle{Anomaly Detection}
|
||||||
|
\begin{columns}[c] % align columns
|
||||||
|
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|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Anomalies are rare, so often only a few datapoints known (e.g. Machine Failure in an Aircraft)
|
||||||
|
|
||||||
|
\item In practice, anomalies might appear that are not known during testing
|
||||||
|
|
||||||
|
\item $\Rightarrow$So train the model only on normal samples
|
||||||
|
|
||||||
|
\item Unsupervised Machine Learning
|
||||||
|
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item What can we say without knowing anomalies?
|
||||||
|
|
||||||
|
\item ''Understand you dataset''
|
||||||
|
|
||||||
|
|
||||||
|
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|
||||||
|
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
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|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{..//prep/03Anomaly_Detection/usup.pdf}
|
||||||
|
\label{fig:prep03Anomaly_Detectionusuppdf}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\end{columns}
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case1/data/004Anomaly Detection.txt
|
||||||
|
\begin{frame}[label=Anomaly Detection]
|
||||||
|
\frametitle{Anomaly Detection}
|
||||||
|
\begin{columns}[c] % align columns
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Anomalies are rare, so often only a few datapoints known (e.g. Machine Failure in an Aircraft)
|
||||||
|
|
||||||
|
\item In practice, anomalies might appear that are not known during testing
|
||||||
|
|
||||||
|
\item $\Rightarrow$So train the model only on normal samples
|
||||||
|
|
||||||
|
\item Unsupervised Machine Learning
|
||||||
|
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item What can we say without knowing anomalies?
|
||||||
|
|
||||||
|
\item ''Understand you dataset''
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{..//prep/04Anomaly_Detection/circle.pdf}
|
||||||
|
\label{fig:prep04Anomaly_Detectioncirclepdf}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\end{columns}
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case1/data/005Anomaly Detection.txt
|
||||||
|
\begin{frame}[label=Anomaly Detection]
|
||||||
|
\frametitle{Anomaly Detection}
|
||||||
|
\begin{columns}[c] % align columns
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Seems easy? Now do this
|
||||||
|
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item in thousands of dimensions
|
||||||
|
|
||||||
|
\item with complicated distributions
|
||||||
|
|
||||||
|
\item and overlap between anomalies and normal points
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{..//prep/05Anomaly_Detection/anomaly_detection.png}
|
||||||
|
\label{fig:prep05Anomaly_Detectionanomaly_detectionpng}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\end{columns}
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case1/data/006AutoML.txt
|
||||||
|
\begin{frame}[label=AutoML]
|
||||||
|
\frametitle{AutoML}
|
||||||
|
\begin{columns}[c] % align columns
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Most machine learning requires Hyperparameter Optimisation
|
||||||
|
|
||||||
|
\item (Find model parameters that result in the best results)
|
||||||
|
|
||||||
|
\item $\Rightarrow$AutoML: Do this automatically as fast as possible
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{..//prep/06AutoML/Download.png}
|
||||||
|
\label{fig:prep06AutoMLDownloadpng}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\end{columns}
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case1/data/007AutoAD.txt
|
||||||
|
\begin{frame}[label=AutoAD]
|
||||||
|
\frametitle{AutoAD}
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item So lets combine both (Auto Anomaly Detection)
|
||||||
|
|
||||||
|
\item $\Rightarrow$Problem
|
||||||
|
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item AutoMl requires Evaluation (loss, accuracy, AUC) to optimize
|
||||||
|
|
||||||
|
\item AD can only be evaluated with regards to the anomalies
|
||||||
|
|
||||||
|
\item $\Rightarrow$no longer unsupervised
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\item So most Anomaly Detection is ''unoptimized''
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case1/data/008Solution 1 Metrics.txt
|
||||||
|
\begin{frame}[label=Solution 1 Metrics]
|
||||||
|
\frametitle{Solution 1 Metrics}
|
||||||
|
\begin{columns}[c] % align columns
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item So how to solve this?
|
||||||
|
|
||||||
|
\item One option: Think of some function to evaluate only the normal points
|
||||||
|
|
||||||
|
\item $\Rightarrow$A bit hard to do in a case study
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{..//prep/08Solution_1_Metrics/circle2.pdf}
|
||||||
|
\label{fig:prep08Solution_1_Metricscircle2pdf}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\end{columns}
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case1/data/009Solution 2 OneShot Learning.txt
|
||||||
|
\begin{frame}[label=Solution 2 OneShot Learning]
|
||||||
|
\frametitle{Solution 2 OneShot Learning}
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item So how to solve this?
|
||||||
|
|
||||||
|
\item One option: ''Just find the best solution directly''
|
||||||
|
|
||||||
|
\item $\Rightarrow$Zero Shot AutoML
|
||||||
|
|
||||||
|
\item Find best practices for hyperparameters
|
||||||
|
|
||||||
|
\item Requires optimisation for each model seperately $\Rightarrow$ matches the case study structure quite well!
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case1/data/010Course.txt
|
||||||
|
\begin{frame}[label=Course]
|
||||||
|
\frametitle{Course}
|
||||||
|
\begin{columns}[c] % align columns
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Basics of Scientific Computing
|
||||||
|
|
||||||
|
\item Basics of AD
|
||||||
|
|
||||||
|
\item Basics of AutoML
|
||||||
|
|
||||||
|
\item Build groups for each algorithm
|
||||||
|
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Choose a set of Hyperparameters
|
||||||
|
|
||||||
|
\item Find ''best practice`s'' for them
|
||||||
|
|
||||||
|
\item Maybe consider more complicated Transformations (Preprocessing, Ensemble)
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\item Compare between groups (best algorithm for current situation)
|
||||||
|
|
||||||
|
\item Evaluate on new datasets
|
||||||
|
|
||||||
|
\item Write a report/Present your work
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{..//prep/09Course/table.png}
|
||||||
|
\label{fig:prep09Coursetablepng}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\end{columns}
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case1/data/011Questions.txt
|
||||||
|
\begin{frame}[label=Questions]
|
||||||
|
\frametitle{Questions}
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Requirements:
|
||||||
|
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item MD Req 1$\Rightarrow$MD Req 8
|
||||||
|
|
||||||
|
\item Basic Python/Math Knowledge
|
||||||
|
|
||||||
|
\item Motivation to learn something new;)
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
\end{document}
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
<titlepage>
|
||||||
Binary file not shown.
|
|
@ -0,0 +1,6 @@
|
||||||
|
Two distributions
|
||||||
|
<l2st>
|
||||||
|
One known (=normal)
|
||||||
|
One unknown (=anomalies)
|
||||||
|
</l2st>
|
||||||
|
Seperate them
|
||||||
Binary file not shown.
|
|
@ -0,0 +1,7 @@
|
||||||
|
Two distributions
|
||||||
|
<l2st>
|
||||||
|
One known (=normal)
|
||||||
|
One unknown (=anomalies)
|
||||||
|
</l2st>
|
||||||
|
Seperate them
|
||||||
|
Problem: few anomalies
|
||||||
|
|
@ -0,0 +1,8 @@
|
||||||
|
Anomalies are rare, so often only a few datapoints known (e.g. Machine Failure in an Aircraft)
|
||||||
|
In practice, anomalies might appear that are not known during testing
|
||||||
|
->So train the model only on normal samples
|
||||||
|
Unsupervised Machine Learning
|
||||||
|
<l2st>
|
||||||
|
What can we say without knowing anomalies?
|
||||||
|
''Understand you dataset''
|
||||||
|
</l2st>
|
||||||
Binary file not shown.
Binary file not shown.
|
|
@ -0,0 +1,8 @@
|
||||||
|
Anomalies are rare, so often only a few datapoints known (e.g. Machine Failure in an Aircraft)
|
||||||
|
In practice, anomalies might appear that are not known during testing
|
||||||
|
->So train the model only on normal samples
|
||||||
|
Unsupervised Machine Learning
|
||||||
|
<l2st>
|
||||||
|
What can we say without knowing anomalies?
|
||||||
|
''Understand you dataset''
|
||||||
|
</l2st>
|
||||||
Binary file not shown.
|
After Width: | Height: | Size: 648 KiB |
|
|
@ -0,0 +1,6 @@
|
||||||
|
Seems easy? Now do this
|
||||||
|
<l2st>
|
||||||
|
in thousands of dimensions
|
||||||
|
with complicated distributions
|
||||||
|
and overlap between anomalies and normal points
|
||||||
|
</l2st>
|
||||||
Binary file not shown.
|
After Width: | Height: | Size: 47 KiB |
|
|
@ -0,0 +1,3 @@
|
||||||
|
Most machine learning requires Hyperparameter Optimisation
|
||||||
|
(Find model parameters that result in the best results)
|
||||||
|
->AutoML: Do this automatically as fast as possible
|
||||||
|
|
@ -0,0 +1,9 @@
|
||||||
|
So lets combine both (Auto Anomaly Detection)
|
||||||
|
->Problem
|
||||||
|
<l2st>
|
||||||
|
AutoMl requires Evaluation (loss, accuracy, AUC) to optimize
|
||||||
|
AD can only be evaluated with regards to the anomalies
|
||||||
|
->no longer unsupervised
|
||||||
|
</l2st>
|
||||||
|
So most Anomaly Detection is ''unoptimized''
|
||||||
|
|
||||||
Binary file not shown.
|
|
@ -0,0 +1,3 @@
|
||||||
|
So how to solve this?
|
||||||
|
One option: Think of some function to evaluate only the normal points
|
||||||
|
->A bit hard to do in a case study
|
||||||
|
|
@ -0,0 +1,5 @@
|
||||||
|
So how to solve this?
|
||||||
|
One option: ''Just find the best solution directly''
|
||||||
|
->Zero Shot AutoML
|
||||||
|
Find best practices for hyperparameters
|
||||||
|
Requires optimisation for each model seperately -> matches the case study structure quite well!
|
||||||
|
|
@ -0,0 +1,12 @@
|
||||||
|
Basics of Scientific Computing
|
||||||
|
Basics of AD
|
||||||
|
Basics of AutoML
|
||||||
|
Build groups for each algorithm
|
||||||
|
<l2st>
|
||||||
|
Choose a set of Hyperparameters
|
||||||
|
Find ''best practice`s'' for them
|
||||||
|
Maybe consider more complicated Transformations (Preprocessing, Ensemble)
|
||||||
|
</l2st>
|
||||||
|
Compare between groups (best algorithm for current situation)
|
||||||
|
Evaluate on new datasets
|
||||||
|
Write a report/Present your work
|
||||||
Binary file not shown.
|
After Width: | Height: | Size: 80 KiB |
|
|
@ -0,0 +1,6 @@
|
||||||
|
Requirements:
|
||||||
|
<l2st>
|
||||||
|
MD Req 1->MD Req 8
|
||||||
|
Basic Python/Math Knowledge
|
||||||
|
Motivation to learn something new;)
|
||||||
|
</l2st>
|
||||||
Binary file not shown.
|
After Width: | Height: | Size: 20 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 989 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 265 KiB |
Loading…
Reference in New Issue