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