On the Spotify platform, interesting features are calculated for each record: danceability; energy; key; loudness; mode; speechiness; acousticness; instrumentality; liveness; valence. Each track for which the following attributes are calculated is assigned to one of the music genres: Trap, Techno, Techhouse, Trance, Psytrance, Dark Trap, DnB (drums and bass), Hardstyle, Underground Rap, Trap Metal, Emo, Rap, RnB, Pop or Hiphop. What intelligent systems could be adapted to assign one of the genres to a newly uploaded piece of music by calculating the listed features?

 Evaluation rules:

1.      Ability to formulate tasks according to the given problem. (1)

a. Understand the problem (identify / explain where uncertainties may arise when developing / implementing a problem solving algorithm, where there are several ways to solve a problem, and it is unclear which way is better if one sees a commercial system solving this kind of problem; the tool was able to resolve). Problem locations requiring a solution must be described. Identify the difficulties in solving the given problem. Which parts of the solution to the problem are not obvious. (0,25)
b. Divide the solution into several tasks. (0,25)
c. Provide clear and comprehensible formulations of tasks for different professionals (meaning not to formulate tasks for different professionals, but to explain tasks so that their meaning, the need to solve them can be seen by the project manager, financier, manager and, for example, HR manager). (0,5)
2.      Ability to provide several reasoned solutions. (2)

a. Name 2-4 ways to solve the problem (to identify which methods or algorithms can be applied to the above problems; only those tasks where the intelligent system is required / possible to be selected). (0,5)
b. Provide arguments (such as a reference to the literature), why the listed solutions could be applied or should work (provide an argument as to why you chose this method for this type of problem. For example, you might find a link to a scientific article describing flower classification with SOM exercise is also a classification exercise with a similar number of attributes (inputs) and classes (outputs). Reference can be given by author and year. Ideally, DOI.) (1,5)
3.      Ability to prove, that your proposed solution is the best. (3)

a. Select the best solution from alternative ones. (0,5)
b. Provide advantages of the selected solution compared to other possible solutions (give reasons why the solution you choose is better than others. The other solutions are worse or less suitable for solving the problem). (2,5)
4.      Clearly explain, how the proposed solution should be implemented by a programmer. (4)

a. Name the inputs and outputs (when formulating the task it is necessary to predict what type and what data will be presented at the solution input and what data will be received at the output, and how these output data should be interpreted. we had one output, and its values ​​of 0 or 1 indicated which class the image belongs to, whose attributes we sent to the entrance). (0.5)
b. Draw a diagram of the solution algorithm (here you should write or draw something similar to the algorithm's diagram, albeit not 100% detailed). (1)
c. Write the formulas, which are needed for implementation together with explanation of variables, used in the formula (e. g. what is variable and where to retrieve it.) One should try to write (or copy from the Internet) formulas that describe mathematically what the intelligent system has to do with the input The formulas used in the training process can be omitted if the training is performed only once.). (2.5)