initial push
This commit is contained in:
Simon Klüttermann
commit
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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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||||
</l2st>
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<e>Seperate them</e>
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||||
</list>
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||||
</que>
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||||
<que>
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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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||||
<que>
|
||||
<list>
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||||
<e>Two distributions</e>
|
||||
<l2st>
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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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||||
</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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||||
</l2st>
|
||||
</list>
|
||||
</que>
|
||||
<que>
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||||
<i f="..//prep/03Anomaly_Detection/usup.pdf" wmode="True"></i>
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</que>
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</split>
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</frame>
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@ -0,0 +1,19 @@
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|||
<frame title="Anomaly Detection">
|
||||
<split>
|
||||
<que>
|
||||
<list>
|
||||
<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>
|
||||
<l2st>
|
||||
<e>What can we say without knowing anomalies?</e>
|
||||
<e>''Understand you dataset''</e>
|
||||
</l2st>
|
||||
</list>
|
||||
</que>
|
||||
<que>
|
||||
<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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@ -0,0 +1,17 @@
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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>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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||||
</l2st>
|
||||
</list>
|
||||
</que>
|
||||
<que>
|
||||
<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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||||
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|||
<frame title="AutoML">
|
||||
<split>
|
||||
<que>
|
||||
<list>
|
||||
<e>Most machine learning requires Hyperparameter Optimisation</e>
|
||||
<e>(Find model parameters that result in the best results)</e>
|
||||
<e>->AutoML: Do this automatically as fast as possible</e>
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||||
</list>
|
||||
</que>
|
||||
<que>
|
||||
<i f="..//prep/06AutoML/Download.png" wmode="True"></i>
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||||
</que>
|
||||
</split>
|
||||
</frame>
|
||||
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|||
<frame title="AutoAD">
|
||||
<list>
|
||||
<e>So lets combine both (Auto Anomaly Detection)</e>
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||||
<e>->Problem</e>
|
||||
<l2st>
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||||
<e>AutoMl requires Evaluation (loss, accuracy, AUC) to optimize</e>
|
||||
<e>AD can only be evaluated with regards to the anomalies</e>
|
||||
<e>->no longer unsupervised</e>
|
||||
</l2st>
|
||||
<e>So most Anomaly Detection is ''unoptimized''</e>
|
||||
</list>
|
||||
</frame>
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||||
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@ -0,0 +1,14 @@
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|||
<frame title="Solution 1 Metrics">
|
||||
<split>
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||||
<que>
|
||||
<list>
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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>
|
||||
</que>
|
||||
<que>
|
||||
<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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||||
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@ -0,0 +1,9 @@
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|||
<frame title="Solution 2 OneShot Learning">
|
||||
<list>
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||||
<e>So how to solve this?</e>
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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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||||
</list>
|
||||
</frame>
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||||
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@ -0,0 +1,23 @@
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|||
<frame title="Course">
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||||
<split>
|
||||
<que>
|
||||
<list>
|
||||
<e>Basics of Scientific Computing</e>
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||||
<e>Basics of AD</e>
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||||
<e>Basics of AutoML</e>
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||||
<e>Build groups for each algorithm</e>
|
||||
<l2st>
|
||||
<e>Choose a set of Hyperparameters</e>
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||||
<e>Find ''best practice`s'' for them</e>
|
||||
<e>Maybe consider more complicated Transformations (Preprocessing, Ensemble)</e>
|
||||
</l2st>
|
||||
<e>Compare between groups (best algorithm for current situation)</e>
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||||
<e>Evaluate on new datasets</e>
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||||
<e>Write a report/Present your work</e>
|
||||
</list>
|
||||
</que>
|
||||
<que>
|
||||
<i f="..//prep/09Course/table.png" wmode="True"></i>
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||||
</que>
|
||||
</split>
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||||
</frame>
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||||
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@ -0,0 +1,10 @@
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|||
<frame title="Questions">
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||||
<list>
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||||
<e>Requirements:</e>
|
||||
<l2st>
|
||||
<e>MD Req 1->MD Req 8</e>
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||||
<e>Basic Python/Math Knowledge</e>
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||||
<e>Motivation to learn something new;)</e>
|
||||
</l2st>
|
||||
</list>
|
||||
</frame>
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||||
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@ -0,0 +1,12 @@
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<plt>
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||||
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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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||||
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||||
</plt>
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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\oldnewlabel\newlabel
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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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\beamer@slide {Anomaly Detection<1>}{2}
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\beamer@slide {Anomaly Detection}{2}
|
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\beamer@slide {fig:prep01Anomaly_Detectionanomaliespdf}{2}
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|
||||
\beamer@slide {Anomaly Detection}{4}
|
||||
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|
||||
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|
||||
\beamer@slide {Anomaly Detection}{5}
|
||||
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|
||||
\beamer@slide {Anomaly Detection<1>}{6}
|
||||
\beamer@slide {Anomaly Detection}{6}
|
||||
\beamer@slide {fig:prep05Anomaly_Detectionanomaly_detectionpng}{6}
|
||||
\beamer@slide {AutoML<1>}{7}
|
||||
\beamer@slide {AutoML}{7}
|
||||
\beamer@slide {fig:prep06AutoMLDownloadpng}{7}
|
||||
\beamer@slide {AutoAD<1>}{8}
|
||||
\beamer@slide {AutoAD}{8}
|
||||
\beamer@slide {Solution 1 Metrics<1>}{9}
|
||||
\beamer@slide {Solution 1 Metrics}{9}
|
||||
\beamer@slide {fig:prep08Solution_1_Metricscircle2pdf}{9}
|
||||
\beamer@slide {Solution 2 OneShot Learning<1>}{10}
|
||||
\beamer@slide {Solution 2 OneShot Learning}{10}
|
||||
\beamer@slide {Course<1>}{11}
|
||||
\beamer@slide {Course}{11}
|
||||
\beamer@slide {fig:prep09Coursetablepng}{11}
|
||||
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|
||||
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|
||||
% Daher steht es hier vor \begin{document}
|
||||
|
||||
\title[AutoML4Rad]{Case Study - AutoML for Robust Anomaly Detection}
|
||||
\author{Simon Kluettermann}
|
||||
\date{\today}
|
||||
|
||||
|
||||
\institute{ls9 tu Dortmund}
|
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%\beamersetuncovermixins{\opaqueness<1>{25}}{\opaqueness<2$\Rightarrow${15}}%here disabled
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\begin{document}
|
||||
|
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|
||||
|
||||
%from file ../case1/data/000.txt
|
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\begin{frame}[label=]
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\frametitle{}
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\begin{titlepage}
|
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|
||||
\centering
|
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{\huge\bfseries \par}
|
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\vspace{2cm}
|
||||
{\LARGE\itshape Simon Kluettermann\par}
|
||||
\vspace{1.5cm}
|
||||
{\scshape\Large Master Thesis in Physics\par}
|
||||
\vspace{0.2cm}
|
||||
{\Large submitted to the \par}
|
||||
\vspace{0.2cm}
|
||||
{\scshape\Large Faculty of Mathematics Computer Science and Natural Sciences \par}
|
||||
\vspace{0.2cm}
|
||||
{\Large \par}
|
||||
\vspace{0.2cm}
|
||||
{\scshape\Large RWTH Aachen University}
|
||||
\vspace{1cm}
|
||||
|
||||
\vfill
|
||||
{\scshape\Large Department of Physics\par}
|
||||
\vspace{0.2cm}
|
||||
{\scshape\Large Insitute for theoretical Particle Physics and Cosmology\par}
|
||||
\vspace{0.2cm}
|
||||
{ \Large\par}
|
||||
\vspace{0.2cm}
|
||||
{\Large First Referee: Prof. Dr. Michael Kraemer \par}
|
||||
{\Large Second Referee: Prof. Dr. Felix Kahlhoefer}
|
||||
|
||||
\vfill
|
||||
|
||||
% Bottom of the page
|
||||
{\large November 2020 \par}
|
||||
\end{titlepage}
|
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\pagenumbering{roman}
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\thispagestyle{empty}
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\null
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||||
\newpage
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\setcounter{page}{1}
|
||||
\pagenumbering{arabic}
|
||||
\end{frame}
|
||||
|
||||
|
||||
%from file ../case1/data/001Anomaly Detection.txt
|
||||
\begin{frame}[label=Anomaly Detection]
|
||||
\frametitle{Anomaly Detection}
|
||||
\begin{columns}[c] % align columns
|
||||
\begin{column}{0.48\textwidth}%.48
|
||||
\begin{itemize}
|
||||
|
||||
\item Two distributions
|
||||
|
||||
\begin{itemize}
|
||||
|
||||
\item One known (=normal)
|
||||
|
||||
\item One unknown (=anomalies)
|
||||
|
||||
|
||||
\end{itemize}
|
||||
\item Seperate them
|
||||
|
||||
|
||||
\end{itemize}
|
||||
\end{column}%
|
||||
\hfill%
|
||||
\begin{column}{0.48\textwidth}%.48
|
||||
\begin{figure}[H]
|
||||
\centering
|
||||
\includegraphics[width=0.9\textwidth]{..//prep/01Anomaly_Detection/anomalies.pdf}
|
||||
\label{fig:prep01Anomaly_Detectionanomaliespdf}
|
||||
\end{figure}
|
||||
|
||||
|
||||
\end{column}%
|
||||
\hfill%
|
||||
\end{columns}
|
||||
|
||||
\end{frame}
|
||||
|
||||
|
||||
%from file ../case1/data/002Anomaly Detection.txt
|
||||
\begin{frame}[label=Anomaly Detection]
|
||||
\frametitle{Anomaly Detection}
|
||||
\begin{columns}[c] % align columns
|
||||
\begin{column}{0.48\textwidth}%.48
|
||||
\begin{itemize}
|
||||
|
||||
\item Two distributions
|
||||
|
||||
\begin{itemize}
|
||||
|
||||
\item One known (=normal)
|
||||
|
||||
\item One unknown (=anomalies)
|
||||
|
||||
|
||||
\end{itemize}
|
||||
\item Seperate them
|
||||
|
||||
\item Problem: few anomalies
|
||||
|
||||
|
||||
\end{itemize}
|
||||
\end{column}%
|
||||
\hfill%
|
||||
\begin{column}{0.48\textwidth}%.48
|
||||
\begin{figure}[H]
|
||||
\centering
|
||||
\includegraphics[width=0.9\textwidth]{..//prep/02Anomaly_Detection/difference.pdf}
|
||||
\label{fig:prep02Anomaly_Detectiondifferencepdf}
|
||||
\end{figure}
|
||||
|
||||
|
||||
\end{column}%
|
||||
\hfill%
|
||||
\end{columns}
|
||||
|
||||
\end{frame}
|
||||
|
||||
|
||||
%from file ../case1/data/003Anomaly 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/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>
|
||||
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|
|
@ -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>
|
||||
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|
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>
|
||||
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|
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>
|
||||
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|
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|
After Width: | Height: | Size: 989 KiB |
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|
After Width: | Height: | Size: 265 KiB |
Loading…
Reference in New Issue