Teaching
Summer semester 2024
- Lecture: Geometric Machine Learning in Quantum Chemistry
- Journal Club: Advanced Machine Learning
Winter semester 2023
- Lecture (by Visiting Professor Dmitry Kobak): Einführung ins Maschinelle Lernen
- Seminar: Transformers, large language models, and their use in physics
- Journal Club: Advanced Machine Learning
Video lectures
Computer Vision: Foundations (Summer Term 2020)
- 1.1 Scope of the Lecture | Introduction
- 1.2 Linear filters | Convolution
- 1.3 Interactive Semantic Segmentation with ilastik
- 2.1 Human Vision | Imaging
- 2.2 Downsampling an Image
- 2.3 Upsampling | Image Interpolation
- 3.1 Shallow vs Deep Learning
- 3.2 Training of a Neural Network (Introduction)
- 4.1 Convolutional Neural Networks | Image Classification
- 4.2 Training of a Neural Network | Optimization
- 4.3 U-net architecture | Semantic Segmentation
- 5.1 Fundamentals of Instance Segmentation
- 5.2 Proposal-based Instance Segmentation
- 5.3 Hough-transform
- 5.4 Instance Segmentation by Similarity Learning
- 6.1 Efficiently Solvable Graph Problems (Introduction)
- 6.2 Shortest Paths | Dijkstra Algorithm
- 6.3 Shortest Paths | 1D Labeling Problems
- 6.4 Shortest Paths | Segmented Least Squares
- 7.1 Dynamic Programming on Trees (Introduction)
- 7.2 Dynamic Programming on Trees | Message Passing
- 7.3 Dynamic Programming on Trees | Applications in Computer Vision
- 8.1 Watershed Algorithm | Clinical Application
- 8.2 Shortest Path vs. Widest Path | Seeded Segmentation
- 8.3 Minimax Paths | Prim's Algorithm
- 8.4 All-pairs Minimax Paths | Minimum Spanning Tree
- 8.5 Seeded Watershed Segmentation | ilastik Demo
- 8.6 Watershed Segmentation | Connection to Deep Learning
- 9.1 Recap of Shortest Path Algorithm
- 9.2 All-pairs Shortest Paths | Distance Product
- 9.3 The Algebraic Path Problem
- 9.4 Infimal Convolution | Euclidean Distance Transform
- 10.1 Tracking: Introduction and Overview
- 10.2 Tracking by Assignment | Min-Cost Flow
- 10.3 (Integer) Linear Programming | Polyhedral Geometry
- 10.4 Total Unimodularity
- 11.1 Optimal Transport: Introduction and Motivation
- 11.2 Discrete Optimal Transport
- 11.3 Discrete Optimal Transport (cont.) | Sinkhorn Iterations
- 11.4 Wasserstein Generative Adversarial Networks
Machine Learning (Winter Term 2019)
- 1. Introduction and PCA
- 2. SVD and KDE
- 3. Mean-Shift and k-Means
- 4. Classification, k-NN, Cross-Validation, and Decision Trees
- 5. Decision Trees and Random Forests
- 6. Bayes Theorem, Statistical Decision Theory, and Quadratic Discriminant Analysis
- 7. Linear Regression
- 8. Regularized Linear Regression (Ridge, Lasso, ...)
- 9. Gaussian Process Regression
- 10. Logistic Regression and Generalized Linear Models
- 11. Perceptron and Multi-Layer Perceptron
- 12. Projection Trick and Function Counting Theorem
- 13. Backpropagation and Neural Network Training
- 14. CNNs and Deep Learning Tricks
- 15. Bayesian Networks/Probabilistic Graphical Models
- 16. Hidden Markov Models
- 17. Kalman Filter
- 18. Guest Lecture (not recorded)
- 19. Multicut and Correlation Clustering
- 20. Cluster Analysis
- 21. Dimension Reduction
Machine Learning for Computer Vision (Winter Term 2017)
- 1. Introduction
- 2. Undirected Probabilistic Graphical Models
- 2.1 MAP & Priors
- 2.2 Markov Random Fields
- 2.3 Gibbs Sampling
- 2.4 MRF as Integer Linear Program (I)
- 2.5 MRF as Integer Linear Program (II)
- 2.6 Tree-Shaped MRF
- 2.7 Belief Propagation
- 2.8 Gaussian MRF (I)
- 2.9 Gaussian MRF (II)
- 3. Neural Networks
- 3.1 Perceptrons
- 3.2 Back Propagation
- 3.3 Introduction to Deep Learning
- 3.4 Deep Learning Architectures
- 3.5 Natural Gradient Optimization (I)
- 3.6 Natural Gradient Optimization (II)
- 3.7 Combining Graphical Models & Neural Networks (I)
- 3.8 Combining Graphical Models & Neural Networks (II)
- 4. Directed Probabilistic Graphical Models
- 4.1 Reinforcement Learning
- 4.2 Policy Gradient
- 6.3 Robotics
Pattern Recognition (Summer Term 2012)
Full playlist on youtube.
1 Introduction
- 1.1 Applications of Pattern Recognition
- 1.2 k-Nearest Neighbors Classification
- 1.3 Probability Theory
- 1.4 Statistical Decision Theory
2 Correlation measures, Gaussian Models
- 2.1 Pearson Correlation
- 2.2 Alternative Correl. Measures
- 2.3 Gaussian Graphical Models
- 2.4 Discriminant Analysis
3 Dimensionality Reduction
4 Neural Networks
- 4.1 History of Neural Networks
- 4.2 Perceptrons
- 4.3 Multilayer Perceptrons
- 4.4 The Projection Trick
- 4.5 Radial Basis Function Networks
5 Support Vector Machines
6 Kernels, Random Forest
7 Regression
- 7.1 Least-Squares Regression
- 7.2 Optimum Experimental Design
- 7.3 Case Study: Functional MRI
- 7.4 Case Study: CT
- 7.5 Regularized Regression
8 Gaussian Processes
- 8.1 Gaussian Process Regression
- 8.2 GP Regression: Interpretation
- 8.3 Gaussian Stochastic Processes
- 8.4 Covariance Function
9 Unsupervised Learning
- 9.1 Kernel Density Estimation
- 9.2 Cluster Analysis
- 9.3 Expectation Maximization
- 9.4 Gaussian Mixture Models
10 Directed Graphical Models
11 Optimization
- 11.1 The Lagrangian Method
- 11.2 Constraint Qualifications
- 11.3 Linear Programming
- 11.4 The Simplex Algorithm
12 Structured Learning
Image Analysis (Summer Term 2013 / 2015).
Full playlist on youtube, including some added material (scroll to last entries).
1 Introduction
2 Patches in Image Analysis
3 Fourier Transformation
- Unitary transformations
- The Fourier Transform
- The Discrete Fourier Transform (DFT)
- 2D-DFT: Application to Images
4 Wavelets
5 Images as Topographic Maps
6 Gaussian Random Fields
7 Fields of Experts, Discrete MRFs
- Factor Graphs
- Fields of Experts
- Discrete-Valued MRFs
- MAP inference via Integer Linear Programming (ILP)
8 Binary pairwise MRFs and Graph Cut
- Integer Linear Programs (continued)
- Pseudo Boolean Functions (PBFs)
- Quadratic PBFs with submodular terms
- Max-Flow / Min-Cut
- Graph Cuts
9 Structured learning
- Introduction
- Example model: Tracking by assignment
- Structured Support Vector Machine (structSVM)
- Structured Learning: Applications