Lecture slides
- Lecture 1 (8.9.2020): Introduction
- Lecture 2 (15.9.2020): Statistical learning theory
- Lecture 3 (22.9.2020): Learning with infinite hypothesis classes
- Lecture 4 (29.9.2020): Linear classification
- Lecture 5 (6.10.2020): Support vector machines
- Lecture 6 (13.10.2020): Kernel methods
- Lecture 7 (27.10.2020): Neural networks
- Lecture 8 (3.11.2020): Ensemble learning
- Lecture 9 (10.11.2020): Feature engineering
- Lecture 10 (17.11.2020): Multi-class classification
- Lecture 11 (24.11.2020): Preference learning
- Lecture 12 (1.12.2020): Predicting multiple and structured labels
- FileLecture 1: Introduction FilePDF document
- FileLecture 2: Statistical learning theory FilePDF document
- FileLecture 4: Linear classification FilePDF document
- FileLecture 5: Support vector machines FilePDF document
- FileLecture 6: Kernel methods FilePDF document
- FileLecture 7: Neural networks FilePDF document
- FileLecture 8: Ensemble learning FilePDF document
- FileLecture 9: Feature engineering FilePDF document
- FileLecture 10: Multi-class classification FilePDF document
- FileLecture 11: Preference Learning FilePDF document