This course provides a two-track path through modern ML.
Wednesdays 12-14 (Fundamentals, 5 ECTS): core concepts and classical methods—ideal if you need a concise, practice-oriented introduction.
Thursdays 14-16 (Advanced, +5 ECTS): deeper and newer topics that build directly on Wednesday’s lecture—recommended for data scientists and anyone taking 10 ECTS.
2 lectures/week: Wed = foundational (x.1), Thu = advanced (x.2).
5 ECTS: attend & pass foundational topics (Wednesday lecture) + foundational assignments.
10 ECTS: attend both lecture days + complete foundational & advanced assignments.
By the end you can:
Frame problems for classification, regression, and unsupervised learning.
Train, tune, and validate models responsibly (avoid leakage; use nested CV).
Understand and implement trees, ensembles, linear & kernel methods.
Build and optimize neural networks (MLP, CNNs, RNNs, Transformers).
Apply representation learning (AE, VAE, contrastive, self-supervised).
Reason about Bayesian and generative modeling (GANs, diffusion).
| Lecture # | Lecture (ID & topic) | Date |
| 1.1 | Introduction (Overview, KNN classifier) | Oct 15 |
| 1.2 | KNN for regression, DNNR | Oct 16 |
| 2.1 | Clustering (k-Means, DBSCAN) | Oct 22 |
| 2.2 | Hierarchical & Soft-Clustering (EM, GMM); Deep Embedded Clustering (DEC); Contrastive Clustering; Spectral Clustering | Oct 23 |
| 3.1 | Linear Models | Oct 29 |
| 3.2 | SVMs; Multinomial Logistic Regression (Softmax); Generalized Linear Models | Oct 30 |
| 4.1 | Principal Component Analysis (Dimensionality Reduction, Covariance Matrix, Gaussian Models) | Nov 05 |
| 4.2 | ICA; Nonlinear Dimensionality Reduction (t-SNE, UMAP) | Nov 06 |
| 5.1 | Model Validation | Nov 12 |
| 5.2 | Hyperoptimization, Ablation studies; metrics for validation; data leakage; Nested Cross-Validation | Nov 13 |
| 6.1 | Decision Trees, Bagging, Random Forest | Nov 19 |
| 6.2 | Ensembling: Extremely Randomized Trees; Rotation/Oblique Decision Trees; NODE (Neural Oblivious Decision Ensembles); DeepGBM (brief) | Nov 20 |
| 7.1 | Boosting (AdaBoost + Viola–Jones); Gradient-Boosted Trees (GBTs) | Nov 26 |
| 7.2 | XGBoost; CatBoost; LightGBM | Nov 27 |
| 8.1 | Multi-Layer Perceptron (classic + modern) | Dec 03 |
| 8.2 | Boltzmann Machines; Deep Boltzmann Machine (DBM); probabilistic modeling; Boltzmann generators; Hopfield networks | Dec 04 |
| 9.1 | Network Optimization (Gradient Descent + Backpropagation) | Dec 10 |
| 9.2 | Optimizers (Adam, RMSProp,…); non-gradient methods? (Evolutionary, Bayesian); Nesterov Accelerated Gradient (NAG); adaptive methods (AdaGrad, RMSProp, Adam) | Dec 11 |
| 10.1 | Convolutional Neural Networks (conv layer types; batch norm; dropout) | Dec 17 |
| 10.2 | Vision models (classification, detection, segmentation, pose); CNN architectures (VGG, ResNet, DenseNet, spatial transformer networks) | Dec 18 |
| 11.1 | Autoencoders & Variational Autoencoders; disentangled representation learning (β-VAE, factorVAE) | Jan 07 |
| 11.2 | Bayesian inference; Variational inference; Bayesian neural networks; MCMC | Jan 08 |
| 12.1 | Generative Adversarial Networks (GANs) | Jan 14 |
| 12.2 | Diffusion; Flow matching | Jan 15 |
| 13.1 | Recurrent Neural Networks (RNNs), LSTMs, GRU | Jan 21 |
| 13.2 | State Space Models (Mamba, Hyena) | Jan 22 |
| 14.1 | Attention & Transformers | Jan 28 |
| 14.2 | Large Language Models | Jan 29 |
| 15.1 | Contrastive Learning, SimCLR | Feb 04 |
| 15.2 | BYOL, I-JEPA, VICReg | Feb 05 |
| 16.1 | Recap + Q&A | Feb 11 |
| 16.2 | Recap + Q&A | Feb 12 |
5 ECTS: weekly assignments and exam, covering fundamental topics only
10 ECTS: weekly assignments and exam, covering fundamental + advanced topics
Comfort with linear algebra, calculus, probability, and Python (NumPy/PyTorch/Scikit-learn).
We provide notebooks and data; coding is required for both tracks.
| Course No | Course Type | Hours |
|---|---|---|
| 19304201 | Vorlesung | 2 |
| 19304202 | Übung | 2 |
| Time Span | 15.10.2025 - 12.02.2026 |
|---|---|
| Instructors |
Paul Hagemann
Manuel Heurich
Tim Landgraf
Jianning Li
|
| 0086c_k150 | 2014, BSc Informatik (Mono), 150 LPs |
| 0086d_k135 | 2014, BSc Informatik (Mono), 135 LPs |
| 0087d_k90 | 2015, BSc Informatik (Kombi), 90 LPs |
| 0088d_m60 | 2015, MSc Informatik (Kombi), 60 LPs |
| 0089b_MA120 | 2008, MSc Informatik (Mono), 120 LPs |
| 0089c_MA120 | 2014, MSc Informatik (Mono), 120 LPs |
| 0207b_m37 | 2015, MSc Informatik (Lehramt), 37 LPs |
| 0208b_m42 | 2015, MSc Informatik (Lehramt), 42 LPs |
| 0458a_m37 | 2015, MSc Informatik (Lehramt), 37 LPs |
| 0471a_m42 | 2015, MSc Informatik (Lehramt), 42 LPs |
| 0556a_m37 | 2018, M-Ed Fach 1 Informatik (Lehramt an Integrierten Sekundarschulen und Gymnasien), 37 LPs |
| 0556b_m37 | 2023, M-Ed Informatik Fach 1 (Lehramt an Integrierten Sekundarschulen und Gymnasien), 37 LP |
| 0557a_m42 | 2018, M-Ed Fach 2 Informatik (Lehramt an Integrierten Sekundarschulen und Gymnasien), 42 LPs |
| 0557b_m42 | 2023, M-Ed Informatik Fach 2 Informatik (Lehramt an Integrierten Sekundarschulen und Gymnasien), 42 LPs |
| 0590a_MA120 | 2019, MSc Data Science, 120 LP |
| 0590b_MA120 | 2021, MSc Data Science, 120 LP |
| Day | Time | Location | Details |
|---|---|---|---|
| Wednesday | 12-14 | T9/Gr. Hörsaal | 2025-10-15 - 2026-02-11 |
| Thursday | 14-16 | T9/Gr. Hörsaal | 2025-10-16 - 2026-02-12 |
| Thursday | 14-16 | 2025-12-04 - 2025-12-18 | |
| Daily | 12-14 | 2025-12-10 - 2025-12-17 |
| Day | Time | Location | Details |
|---|---|---|---|
| Monday | 14-16 | T9/SR 005 Übungsraum | Übung 01 |