I have uploaded most of my “Machine Learning” lecture to YouTube.

The slides are in English, but the audio is in German.

Some very basic contents (e.g., a demo of standard k-means clustering) were left out from this advanced class, and instead only a link to recordings from an earlier class were given. In this class, I wanted to focus on the improved (accelerated) algorithms instead. These are not included here (yet). I believe there are some contents covered in this class you will find nowhere else (yet).

The first unit is pretty long (I did not split it further yet). The later units are shorter recordings.

ML F1: Principles in Machine Learning

ML F2/F3: Correlation does not Imply Causation & Multiple Testing Problem

ML F4: Overfitting – Überanpassung

ML F5: Fluch der Dimensionalität – Curse of Dimensionality

ML F6: Intrinsische Dimensionalität – Intrinsic Dimensionality

ML F7: Distanzfunktionen und Ähnlichkeitsfunktionen

ML L1: Einführung in die Klassifikation

ML L2: Evaluation und Wahl von Klassifikatoren

ML L3: Bayes-Klassifikatoren

ML L4: Nächste-Nachbarn Klassifikation

ML L5: Nächste Nachbarn und Kerndichteschätzung

ML L6: Lernen von Entscheidungsbäumen

ML L7: Splitkriterien bei Entscheidungsbäumen

ML L8: Ensembles und Meta-Learning: Random Forests und Gradient Boosting

ML L9: Support Vector Machinen - Motivation

ML L10: Affine Hyperebenen und Skalarprodukte – Geometrie für SVMs

ML L11: Maximum Margin Hyperplane – die “breitest mögliche Straße”

ML L12: Training Support Vector Machines

ML L13: Non-linear SVM and the Kernel Trick

ML L14: SVM – Extensions and Conclusions

ML L15: Motivation of Neural Networks

ML L16: Threshold Logic Units

ML L17: General Artificial Neural Networks

ML L18: Learning Neural Networks with Backpropagation

ML L19: Deep Neural Networks

ML L20: Convolutional Neural Networks

ML L21: Recurrent Neural Networks and LSTM

ML L22: Conclusion Classification

ML U1: Einleitung Clusteranalyse

ML U2: Hierarchisches Clustering

ML U3: Accelerating HAC mit Anderberg’s Algorithmus

ML U4: k-Means Clustering

ML U5: Accelerating k-Means Clustering

ML U6: Limitations of k-Means Clustering

ML U7: Extensions of k-Means Clustering

ML U8: Partitioning Around Medoids (k-Medoids)

ML U9: Gaussian Mixture Modeling (EM Clustering)

ML U10: Gaussian Mixture Modeling Demo

ML U11: BIRCH and BETULA Clustering

ML U12: Motivation Density-Based Clustering (DBSCAN)

ML U13: Density-reachable and density-connected (DBSCAN Clustering)

ML U14: DBSCAN Clustering

ML U15: Parameterization of DBSCAN

ML U16: Extensions and Variations of DBSCAN Clustering

ML U17: OPTICS Clustering

ML U18: Cluster Extraction from OPTICS Plots

ML U19: Understanding the OPTICS Cluster Order

ML U20: Spectral Clustering

ML U21: Biclustering and Subspace Clustering

ML U22: Further Clustering Approaches