Department of Computer Science and Technology

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)