Teaching GuideTerm
Faculty of Computer Science
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Máster Universitario en Intelixencia Artificial
 Subjects
  Data Engineering
   Assessment
Methodologies Competencies Description Qualification
Supervised projects A17 B2 B3 B6 B7 B8 C7 C8 Defense of the solution proposed by the student or oral
presentation of the developed solution.
30
Practical test: A17 B2 B5 B7 C3 Several assessment tests will be conducted in order to evaluate
the understanding of the knowledge exposed in the classes of
theory and/or practical. These tests can not be repeated in the second evaluation call.
30
Problem solving A17 B2 B4 B7 C7 C9 The evaluation of the autonomous work
will include the submission of a report and a defense
in which the students explain their developments and
conclusions in front of the teacher and the classroom.
40
 
Assessment comments
FIRST AND SECOND EVALUATION CALLS [Assisting and Non-assisting students]
Final grade = 0,30 * Project based learning + 0,30 * Laboratory practical tests + 0,40 * Autonomous problem solving

Non-assisting students will complete the same assignments and tests than assisting students.

FINAL GRADES
To pass the course in any of the evaluation calls, the final grade must be equal or greater than 5 (from a total of 10), obtaining a minimum score of 5 (out of 10) in each of the evaluation parts.

In the second opportunity the laboratory practical tests cannot be repeated, so there is no minimum score in this part.

ADDITIONAL REMARKS
If plagiarism is detected in any of the works (essays or project), the final grade will be "Suspenso" (0) and the situation will be notified to the School's Board to take the appropriate disciplinary actions. 

If translation errors cause any contradictions between the various versions of this syllabus, the English will be the prevailing version.
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