SGN-41007 Pattern Recognition and Machine Learning
- First lecture will be on January 8th at 10:15-12:00 in room RI207.
- The registration for weekly exercise groups open on January 8th (13:00) and closes just before the first exercise group (Monday 15th at 12:59). Reserve your seat early enough.
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):
- Mondays at 10-12 in RI207
- Wednesdays at 12-14 in RI207
A video recording of each lecture will be provided below. Links to slides
and videos appear after each lecture.
- Monday 8.1.2018: Course organization, Introduction to Python.
- Wednesday 10.1.2018: Estimation Theory.
- Monday 15.1.2018: Estimation Theory. Detection Theory.
- Wednesday 17.1.2018: Detection Theory. ROC and AUC.
- Monday 22.1.2018: Classification. K-NN classifier. Using
- Wednesday 24.1.2018: Linear Classifiers. The LDA and the SVM. Kernel trick for the SVM. Logistic Regression.
- Monday 29.1.2018: Random Forest. Other ensemble methods in sklearn:
ExtraTreesClassifier, AdaBoostClassifier and
- Wednesday 31.1.2018: Neural networks.
- Monday 5.2.2018: Convolutional networks and deep learning.
- Wednesday 7.2.2018: ConvNets, Recurrent nets.
- Monday 12.2.2018: Performance assessment: Cross-validation,
- Wednesday 14.2.2018: Regularization, feature selection.
- Monday 19.2.2018: Unsupervised methods
- 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).
- Monday 13-15 in TC303
- Monday 16-18 in TC303
- Tuesday 10-12 in TC303
- Tuesday 12-14 in TC303
- Tuesday 14-16 in TC303
- Wednesday 10-12 in TC303)
- Wednesday 16-18 in TC303
- Thursday 10-12 in TC303
- Thursday 16-18 in TC303
- Friday 14-16 in TC303
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.
The following are required to pass the course:
- 60% of exercise assignments solved. (70% -> +1 exam point; 80% -> +2
exam points; 90% -> +3 exam points).
- Project assignment, which is organized in the form of a pattern recognition
competition (TBA later).
- Written exam (max. 30 pts; 15 to pass).
- Many topics of the course are also covered in Hastie et al., The
Elements of Statistical Learning, Springer 2009. Free PDF here.
- Scikit-learn documentation.
- If you forgot to register for the course, send email to the teacher (firstname.lastname@example.org)
- If you are unable to attend some exercises, it is possible to request a special permission to submit solutions via email. This can only happen for a good reason, and must be requested before the course starts by email (email@example.com). After the course starts (Jan 8, at 10:00), it's not possible to join this arrangement anymore.
- If you are unable to attend ANY exercises, request a separate assignment via email (firstname.lastname@example.org).