- HW01 (14.02) - Introductory homework (Happy Valentine's Day!)
- HW02 (21.02) - Frequent Itemsets and Association Rules. R-script for the exercise 5.
- HW03 (28.02) - Association Rules and Interestingness measures.
- HW04 (07.03) - Preprocessing and Descriptive Statistics. First version of iris dataset with missing data and Second version of iris dataset with missing data for the exercise 4.
- HW05 (14.03) - Descriptive Statistics II. Use data.txt for the third task and USArrests.txt for the bonus (description of the dataset: http://stat.ethz.ch/R-manual/R-devel/library/datasets/html/USArrests.html).
- HW06 (21.03) - Clustering. Use progress.txt for the exercise 7, it has missing values that you should substitute with 0.
- HW07 (26.03) - Clustering II. Row IDs' ordering for task 3 and 4.
- HW08 (04.04) - sample data (first load it in .txt format and then change the format to .arff on your computer, this how Weka will recognize it:)) for task 5.
- HW09 (11.04) - my_mails.txt and my_mails.arff for the SPAM classifier in task 6.
- HW10 (25.04) - code.r and chords.zip for the bonus exercise.
- HW11 (09.05) - email_virus.txt and undirected_real_world_graph.txt.
- HW12 (15.05)
A little bit of rules:
- Points for homeworks will be here
- 70% of practice sessions are obligatory to attend in order to pass the course
- Homeworks should be submitted in pdf + script in language of your preference
- Homeworks should be submitted before 23:59 at the day before practice session e.g. for those who attend Wednesday practice sessions it is important to submit homework before 23:59 on Tuesday and so on.