SGN-41007 Pattern Recognition and Machine Learning

What's new?


Most of the material is collected in our GitHub repo. Note however, that they are updated during the course, and some material may be from the previous year.


There are two lectures every week (total 14 lectures: first time 8.1.2017, last time 21.2.2017):

A video recording of each lecture will be provided below. Links to slides and videos appear after each lecture.

  1. Monday 8.1.2018: Course organization, Introduction to Python.
  2. Wednesday 10.1.2018: Estimation Theory.
  3. Monday 15.1.2018: Estimation Theory. Detection Theory.
  4. Wednesday 17.1.2018: Detection Theory. ROC and AUC.
  5. Monday 22.1.2018: Classification. K-NN classifier. Using Scikit-learn.
  6. Wednesday 24.1.2018: Linear Classifiers. The LDA and the SVM. Kernel trick for the SVM. Logistic Regression.
  7. Monday 29.1.2018: Random Forest. Other ensemble methods in sklearn: ExtraTreesClassifier, AdaBoostClassifier and GradientBoostingClassifier.
  8. Wednesday 31.1.2018: Neural networks.
  9. Monday 5.2.2018: Convolutional networks and deep learning.
  10. Wednesday 7.2.2018: ConvNets, Recurrent nets.
  11. Monday 12.2.2018: Performance assessment: Cross-validation, leave-one-out, etc.
  12. Wednesday 14.2.2018: Regularization, feature selection.
  13. Monday 19.2.2018: Unsupervised methods (K-means, PCA).
  14. Wednesday 21.2.2018: Hyperparameter selection, application examples. Visiting lecture from a company.


Exercises will appear here, and sessions are once every week. First exercises will be held on 15.1.2018.

There are 10 exercise groups, of which you choose one. The times are still subject to change.

Registration for the groups is required. Registration ends in POP on Jan 15th at 12:59 (1 min before first exercise group).

Exercises consist of theory and computer exercises. You can use the classroom computer or your own laptop. Installation of necessary software is straightforward: Anaconda Python distribution should contain all necessary packages.

To Pass

The following are required to pass the course:

Additional Material



Teacher: Heikki Huttunen.

          HTML 4.01 Transitional