This course provides in-depth understanding of the most fundamental algorithms in statistical pattern recognition and machine learning (including Deep Learning) as well as concrete tools (as Python source code) to PhD students for their work.

Course contents

  • Introduction to Pattern Recognition
  • Linear Regression
  • k-Nearest Neighbours (kNN)
  • Logistic Regression
  • Regularisation, Capacity and Overfitting
  • Decision Trees
  • Boosting
  • Dimensionality Reduction (PCA, LDA, t-SNE)
  • Clustering and Distribution Modelling (k-Means, Gaussian Mixtures and Single Linkage)
  • Neural Networks: Multi-layer Perceptrons and Back-propagation
  • Deep Learning: Basics, Architectures, Fine-tuning
  • Support Vector Machines