Course pages 2018–19
Machine Learning
Lecture notes
- Lecture 1: Introduction (lecture, print)
- Lecture 2: Linear and Logistic Regression (lecture, print)
- Lecture 3: Support Vector Machines (lecture, print)
- Lecture 4: SVMs: Kernels, Multiple Classes, Applications (lecture, print)
- Lecture 5: Logic, Reasoning and Machine Learning (lecture, print)
- Practical (lecture 6): SVM
- Lecture 7: Graph Clustering (lecture, print)
- Lecture 8: Spectral Clustering (lecture, print)
- Lecture 9: Online Learning (lecture, print)
- Lecture 10: AdaBoost (lecture, print)
- Lecture 11+12: Decision Trees, Random Forest, Boosting (lecture)
- Lecture 13: Neural Networks (Deep Learning) (lecture)