Core content

  • Statistical Signal Processing: Estimation theory; Maximum likelihood; Estimation of signal parameters (e.g., phase, amplitude and frequency).
  • Detection theory; Receiver Operating Characteristics; Neyman-Pearson decision rule and relation to machine learning.
  • Linear models: regression and classification, support vector machines, logistic regression, regularization.
  • Modern tools: Random forests, Bagging, Boosting, Stacking, Deep Learning
  • Performance evaluation, cross-validation, bootstrapping
  • Implementations in Python: 1) Scikit-learn, 2) Keras

Learning outcomes

Students understand principles of selected statistical, pattern recognition and machine learning approaches in signal processing related problems. Student can apply the methods to real problems using modern Python tools such as Scikit-Learn and Keras. For more details, see last year slides and videos at

University webpage of the course