In this course we study statistical pattern recognition and machine learning.

The subjects covered are:

• General principles of data analysis: overfitting, bias-variance trade-off, model selection, regularization, the curse of dimensionality.
• Linear statistical models for regression and classification.
• Clustering and unsupervised learning.
• Support vector machines.
• Neural networks and deep learning.

Knowledge of elementary probability theory, statistics, multivariable calculus and linear algebra is presupposed.

Course form
Lectures and computer lab sessions.

Literature

• Book: Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
• Book: Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016.
  Book URL: http://www.deeplearningbook.org.
• Possibly additional literature in the form of research papers, book chapters, etcetera.