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Unsupervised Machine Learning

Unsupervised Machine Learning. Claudio Duran, Cannistraci lab, BIOTEC Robert Haase, Myers lab, MPI CBG Mahmood Nazari, ABX- Cro / Schroeder lab, BIOTEC Martin Weigert, Myers lab, MPI CBG https://www.biotec.tu-dresden.de/research/cannistraci/lecture.html. Python RECAP.

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Unsupervised Machine Learning

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  1. Unsupervised Machine Learning Claudio Duran, Cannistraci lab, BIOTEC Robert Haase, Myers lab, MPI CBG Mahmood Nazari, ABX-Cro / Schroeder lab, BIOTEC Martin Weigert, Myers lab, MPI CBG https://www.biotec.tu-dresden.de/research/cannistraci/lecture.html

  2. Python RECAP How to read a .csv file?

  3. Python RECAP 0 1 2 3 4 5 6 7 8 9 Manipulating pandas dataframes myList = [2,7,3,9,8,1,5,6,0,4] [7,3,9,8,1,5] myList[1:7] = ? [7,9,1] myList[1:7:2] = ? [1,5,6,0,4] myList[5:] = ? Rows Columns X[i,j] =

  4. Today... • What is machine learning? • Where do we find it? • Unsupervised machine learning • Clustering • Dimensionality Reduction • Measurments for performance (Classification) • Exercises • MNIST

  5. Machine learning Use of AI to provide systems the ability to learn from experiences. • Search engines • Anti-spam softwares SPAM • Credit card transactions • Digital cameras (face recognition)

  6. Machine learning • Machine Learning • Supervised • Unsupervised • Reinforced • Regression • Classification • Dimensionality reduction • Clustering • Learning by reward • Labels/response known • Labels/response unknown • GO game

  7. Unsupervised machine learning Informative way to visualize the data? Detect subgroups or patterns among the variables or observations?

  8. Dimensionality Reduction Objective: to reduce the number of variables in consideration How? Most common dimensionality reduction technique: Principal Component Analysis (PCA)

  9. PCA Finds a sequence of linear combinations of the variables that have maximal variance and are mutually uncorrelated.

  10. PCA in python Never forget to import the library You have to create a pca variable with the number of desired components fit_transform is the function to call to apply the PCA transformation

  11. PCA in python

  12. ISOMAP Non-linear dimensionality reduction

  13. ISOMAP Like in PCA 

  14. Clustering Process of grouping a set of similar objects into the same classes How many clusters?

  15. Clustering

  16. K-Means Also known as K-Means, is an algorithm that group objects based on attributes into K number of groups. 1.- Place K centroids in random positions. 2.- Assign point X to the closest cluster (min D). 3.- New centroids are the average of the current point in cluster C 4.- Repeat until convergence (none of clusters assignment changes)

  17. K-Means Fit is the function to apply the clustering method Number of clusters we are looking for “K”

  18. K-Means

  19. K-Means

  20. DBSCAN Also known as Density-Based Spatial Clustering of Applications with Noise Density-based clustering locates regions of high density that are separated from one another by regions of low density Density = number of points within a specified radius (Eps)

  21. DBSCAN Concepts: Core point: point that has more than a specified number of points (MinPts) within Eps Border point: has fewer than MinPts within Eps, but is in the neighborhood of a core point Noise point: any point that is not core point or border point If two core points are in their neighbourhood(within a distance Eps of one another) they are put in the same cluster

  22. DBSCAN Eps and MinPts as input arguments Same function as Kmeans

  23. DBSCAN

  24. DBSCAN

  25. Performance measurements Usually applied in binary classification tasks (supervised learning) -Accuracy -Sensitivity (or recall or true positive rate [TPR]) -Specificity (or true negative rate [TNR]) -Precision (or positive predicted values) -F-score

  26. Performance measurements All positive samples Predicted as positive

  27. Performance measurements Accuracy Sensitivity (recall) Precision Specificity How to check clustering performance? F-score

  28. Exercises!

  29. Exercises MNIST data hand writing 28x28 pixels Gray value scale Transformed into an array of 1x784

  30. Exercises 1.- Load the MNIST data into a dataframe with pandas 2.- Apply Dimensionality Reduction to the data (PCA and ISOMAP) EXTRA: find the MDS dimensionality reduction technique and apply it [Hint: sklearn library, google] 3.- Show both results in a scatter plot and colour the point according to the label (number) [do not forget to add a legend to the plot] 4.- check If the clustering methods are able to retrieve the number of classes present in the data (10 classes). Try both Kmeans and DBSCAN 5.- check the performance of the clustering methods in retrieving the classes by measuring the accuracy (HINT: good predicted samples/total of samples)

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