Practical Machine Learning Unsupervised Learning Sven Mayer Unsupervised

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Practical Machine Learning Unsupervised Learning Sven Mayer

Practical Machine Learning Unsupervised Learning Sven Mayer

Unsupervised Learning • Data Input “Training” Programm (Model) Not needed: • Lables (Classes) •

Unsupervised Learning • Data Input “Training” Programm (Model) Not needed: • Lables (Classes) • Continues values 2 Sven Mayer

Unsupervised Learning What can we learn just buy looking at the data? �� ��

Unsupervised Learning What can we learn just buy looking at the data? �� �� 3 Sven Mayer

Unsupervised Learning What can we learn just buy looking at the data? �� ��

Unsupervised Learning What can we learn just buy looking at the data? �� �� 4 Sven Mayer

Learning Strategies Supervised Learning Unsupervised Learning Discrete Classification or Categorization Clustering Continuous Regression Dimensionality

Learning Strategies Supervised Learning Unsupervised Learning Discrete Classification or Categorization Clustering Continuous Regression Dimensionality reduction 5 Sven Mayer

Unsupervised Learning Methods § § § Hierarchical clustering K-means clustering Principal Component Analysis (PCA)

Unsupervised Learning Methods § § § Hierarchical clustering K-means clustering Principal Component Analysis (PCA) Singular Value Decomposition Independent Component Analysis …. 6 Sven Mayer

Clustering Unsupervised Learning 7 Sven Mayer

Clustering Unsupervised Learning 7 Sven Mayer

Unknown Data 8 Sven Mayer

Unknown Data 8 Sven Mayer

Clustering K-means clustering § Uncovering “structure” in unlabeled data 9 Sven Mayer

Clustering K-means clustering § Uncovering “structure” in unlabeled data 9 Sven Mayer

Clustering K-means clustering 10 Sven Mayer

Clustering K-means clustering 10 Sven Mayer

Clustering K-means clustering ? 11 Sven Mayer

Clustering K-means clustering ? 11 Sven Mayer

Clustering Cluster Count? 12 Sven Mayer

Clustering Cluster Count? 12 Sven Mayer

Dimensionality Reduction Unsupervised Learning 13 Sven Mayer

Dimensionality Reduction Unsupervised Learning 13 Sven Mayer

Dimensionality Reduction Principal Component Analysis (PCA) § Transforming high-dimensional data into a low-dimensional space

Dimensionality Reduction Principal Component Analysis (PCA) § Transforming high-dimensional data into a low-dimensional space Sven Mayer

Dimensionality Reduction Principal Component Analysis (PCA) § Transforming high-dimensional data into a low-dimensional space

Dimensionality Reduction Principal Component Analysis (PCA) § Transforming high-dimensional data into a low-dimensional space 15 Sven Mayer

Dimensionality Reduction Principal Component Analysis (PCA) § Transforming high-dimensional data into a low-dimensional space

Dimensionality Reduction Principal Component Analysis (PCA) § Transforming high-dimensional data into a low-dimensional space 16 Sven Mayer

Dimensionality Reduction Principal Component Analysis (PCA) 17 Sven Mayer

Dimensionality Reduction Principal Component Analysis (PCA) 17 Sven Mayer

Dimensionality Reduction Principal Component Analysis (PCA) 18 Sven Mayer

Dimensionality Reduction Principal Component Analysis (PCA) 18 Sven Mayer

Dimensionality Reduction Principal Component Analysis (PCA) 19 Sven Mayer

Dimensionality Reduction Principal Component Analysis (PCA) 19 Sven Mayer

Conclusion Unsupervised Learning § Clustering § Finding meaning in unlabeled data § Dimensionality Reduction

Conclusion Unsupervised Learning § Clustering § Finding meaning in unlabeled data § Dimensionality Reduction § Transforming high-dimensional data into a low-dimensional space § Reducing the data to more essential features. 20 Sven Mayer

License This file is licensed under the Creative Commons Attribution-Share Alike 4. 0 (CC

License This file is licensed under the Creative Commons Attribution-Share Alike 4. 0 (CC BY-SA) license: https: //creativecommons. org/licenses/by-sa/4. 0 Attribution: Sven Mayer

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