Exercises for the laboratory classes can be found under this git repository.
You can find e-portal course page under this link. Password to sign up will be handed out during the classes.
- exercises are graded during classes after which students receives a grade. This grade accounts both for the realized exercise as well as answering questions,
- final grade is a arithmetic average calculated for all grades,
- one absence is admissible, however an exercise for given laboratory class has to be submitted. It can be submitted in any next classes or during the last one. In justified cases, a greater number of absences is allowed in special cases, which are considered individually,
- short test or so-called entry tests that allow you to participate in laboratories can take place,
- plagiarism as well as non-independent work are unacceptable. In the case of their occurrence, the laboratory exercise is graded with 2.0. In addition, it is possible to fail the course in the event of a gross violation of this point. The work can be verified with the help of anti-plagiarism systems,
- completed tasks should be submitted via the e-portal. Uploading of all tasks via eportal is mandatory. Failing to do so will lead to failing the laboratory exercise.
Some additional material can be found below
The dataset comes as CSV file containing numerical data. It contains five columns. First four columns are considered to be features while the last, fifth column is considered to be a label (one of two classes).
Archive with sample images for training an image classifier.