Repository includes README.md with clear instructions on how to easily install prerequisites and run all the analysis. The code must be runnable and output results must be similar to the ones reported in the report. If your training takes a lot of time, include pre-trained models in your repository. It is not the idea to run everything, just some parts and check the code to get a feeling if provided source code is runnable or there is something missing.

Report follows suggested structure (abstract, introduction, related work, methods, results and discussion, conclusion) and is roughly maxed to 8 pages long. The style is the same as defined in the project introduction slides. The report concisely describes data preprocessing, features extraction and approaches/algorithms (at least 3 approaches). Results and discussion show the results and comments on the performance of algorithms. Discussion also points out where the algorithms do not work well and where they achieve good performance. In the conclusion the authors should point out shortcomings of their approaches and give at least one idea for improvements in the future. When reading the report, you could get a feeling that authors know what they are doing, sensibly selected approaches and critically explain and discuss their results. Assign points as follows: 5 points: abstract, introduction and related work5 points: clear description of selected datasets and analysis; 10 points: clear description of methods/algorithms used (at least 3) and adequate discussion of results.