SGN-41007 Pattern Recognition and Machine Learning

What's new?


There are two lectures every week (total 14 lectures: first time 21.10.2019, last time 5.12.2019):

A video recording of each lecture will be provided below. Links to slides and videos appear after each lecture. There is also a live broadcast, that should be accessible via the Echo360 service. Recorded videos are available in the below list without a need to login.

  1. Monday 21.10.2019: Course organization, Introduction to Python. [video]
  2. Thursday 24.10.2019: Estimation Theory. [video]
  3. Tuesday 29.10.2019: Detection Theory. ROC and AUC. [video]
  4. Thursday 31.10.2019: Precision and Recall. Classification: The K-NN classifier and linear classifiers. [video]
  5. Tuesday 5.11.2019: Linear Classifiers: The LDA and the role of projection. [video]
  6. Thursday 7.11.2019: Linear Classifiers: SVM and the kernel trick; Logistic Regression. [video]
  7. Tuesday 12.11.2019: Neural networks. [video]
  8. Thursday 14.11.2019: Convolutional networks. [video]
  9. Tuesday 19.11.2019: Convolutional networks, Recurrent nets. [video]
  10. Thursday 21.11.2019: Recurrent networks. Applications of deep learning. [video]
  11. Tuesday 26.11.2019: Recurrent networks. Applications of deep learning. [video]
  12. Thursday 28.11.2019: Random Forest. Other ensemble methods in sklearn: ExtraTreesClassifier, AdaBoostClassifier and GradientBoostingClassifier. [video]
  13. Tuesday 3.12.2019: Performance assessment: Cross-validation. Regularization, feature selection. [video]
  14. Thursday 5.12.2019: Visiting lectures from companies: Scandit Xiaomi and VTT. [video]



Exercise tasks will appear below, and sessions are once every week.

  1. Exercise 28.10. - 1.11.: Questions. Solve Python exercises in the exercise class. Solve pen and paper tasks beforehand.
  2. Exercise 4.11. - 8.11.: Questions.
  3. Exercise 11.11. - 15.11.: Questions.
  4. Exercise 18.11. - 22.11.: Questions.
  5. Exercise 25.11. - 29.11.: Questions. (updated on the evening of Nov 21st)
  6. Exercise 2.12. - 5.12.: Questions.

Exercise scores here.

Groups take place at the following times:

Registration for the groups is required. 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.


Course requirements also include a programming assignment, which is organized as a competition (see above).

To Pass

The following are required to pass the course:

Additional Material



Teacher: Heikki Huttunen.