Collection of my materials from the Machine Learning course at TUM, which I took in the 24/25W semester.
Assignments
| Assignment name | Page Link |
|---|---|
| 1. Math refresher | |
| 2. Decision Trees & KNN | |
| 3. Probabilistic Inference | Optimising Likelihoods - Monotonic Transforms Properties of MLE and MAP |
| 4. Linear Regression | |
| 5. Linear Classification | |
| 6. Optimization | |
| 7. Deep Learning 1 | Numerical stability of softmax |
| 8. Deep Learning 2 | Nonlinearity of deep learning |
| 9. SVM | |
| 10. Dimensionality Reduction 1 | Principal Component Analysis (PCA) |
| 11. Dimensionality Reduction 2 | t-SNE Autoencoders |
| 12. Clustering |