Course pages 2017–18

# Machine Learning and Algorithms for Data Mining

### Lecture notes

- Lecture 1: Introduction (lecture, print)
- Lectures 2, 3 and 4: Support Vector Machines (lecture, print)
- Lecture 5: Logic, Reasoning and Machine Learning (lecture, print)
- Practical (lectures 6 and 7): SVM
- Lectures 8 and 9: Spectral graph theory and graph clustering (lecture, print)
- Addition: Spectral graph clustering demonstration lecture.
- Lecture 10: Concentration of Measure and Dimensionality Reduction (lecture, print).
- Lecture 11: Random Walks, Page-Rank and Local Clustering (lecture, print).
- Lecture 12: Decision trees and random forests (lecture, print).
- Lecture 13: Random forests and boosting (lecture, print).
- Lecture 14: Neural network and deep learning (lecture, print).
- Lecture 15: Neural network and deep learning (lecture, print).
- Practical (lectures 16 and 17): Deep neural networks