Requirements and grading of the homeworks:

• In order to pass the course 70% of practice sessions are obligatory to attend.
• Each week after the lecture there is a homework assignment that gives maximum of 5 points (+ extra tasks with bonus points).
• Homeworks should be submitted in pdf + script in programming language of your preference and uploaded here.
• The deadline of the homeworks for all the groups is 23:59 on Sunday 10 days after the lecture, i.e if the lecture is on 12th of February, the deadline of the homework is 22nd of February.
• NB! Homeworks submitted after the deadline will not be graded nor corrected.
• Points for homeworks will be posted here.

Weekly homeworks:

• Dataset for R-tutorial is here, and description to this dataset is here.

HW 0 - Week of Feb 16.-20., TA-s will give a in-class tutorial on R. Please attend!

HW 1 (due Feb 22nd) Introduction (probability, R, business).

• NB! The deadline for HW submissions is Sunday, Feb 22nd.
• NB! Monday, Feb 23rd is officially a holiday at University (due Independence day on 24th). Monday group people would have to attend either the Wednesday or Friday groups. More info from TA-s.

HW 2 (due March 1st) Frequent itemsets, Apriori, FP-tree, R - arules

HW 3 (due March 8th) Association rules continued...

HW 4 (due March 15th) Association rule interestingness, elements of descriptive analysis.

HW 5 (due March 22nd) Descriptive analysis.

HW 6 (due March 29th) Descriptive analysis and visualisation

HW 7 (due April 5th) Visualisation, regression, clustering ...

HW 8 (due April 12th) Clustering ...

HW 9 (due April 19th) Clustering continued...

HW 10 (due April 26th) Clustering, Seriation, ...

HW 11 (due May 3rd) Classification - scores

HW 12 (due May 17th) Machine learning...