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Crčdits | Dept. |
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7.5 (6.0 ECTS) | CS |
Responsable: | (-) |
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In this course, students should gain an understanding of the concepts behind data mining, its goals, techniques and applications, with a special focus on the application to massive data sets. These applications are experiencing a rapid growth in astronomy, marketing and genomics, among other disciplines, and demand supercomputing architectures and scalable algorithms. The course will have a practical side, with assignments using different data sets and techniques, such as advanced visualization, statistical machine learning and model optimization.
Hores estimades de:
T | P | L | Alt | L Ext. | Est | A Ext. |
Teoria | Problemes | Laboratori | Altres activitats | Laboratori extern | Estudi | Altres hores fora d'horari fixat |
|
T | P | L | Alt | L Ext. | Est | A Ext. | Total | ||
---|---|---|---|---|---|---|---|---|---|---|
2,0 | 0 | 0 | 0 | 0 | 2,0 | 0 | 4,0 |
|
T | P | L | Alt | L Ext. | Est | A Ext. | Total | ||
---|---|---|---|---|---|---|---|---|---|---|
6,0 | 2,0 | 0 | 0 | 5,0 | 6,0 | 0 | 19,0 |
|
T | P | L | Alt | L Ext. | Est | A Ext. | Total | ||
---|---|---|---|---|---|---|---|---|---|---|
4,0 | 4,0 | 4,0 | 0 | 10,0 | 4,0 | 0 | 26,0 |
|
T | P | L | Alt | L Ext. | Est | A Ext. | Total | ||
---|---|---|---|---|---|---|---|---|---|---|
6,0 | 4,0 | 8,0 | 0 | 10,0 | 4,0 | 0 | 32,0 |
|
T | P | L | Alt | L Ext. | Est | A Ext. | Total | ||
---|---|---|---|---|---|---|---|---|---|---|
6,0 | 4,0 | 10,0 | 0 | 15,0 | 10,0 | 0 | 45,0 |
Total per tipus | T | P | L | Alt | L Ext. | Est | A Ext. | Total |
24,0 | 14,0 | 22,0 | 0 | 40,0 | 26,0 | 0 | 126,0 | |
Hores addicionals dedicades a l'avaluació | 0 | |||||||
Total hores de treball per l'estudiant | 126,0 |
Classes building up theoretical and methodological concepts in a structured fashion. Problem-oriented classes focusing on a set problem assignments. Laboratory classes focusing on co-operative work and practical applications in order to consolidate concepts, skills and competencies.
The course will be evaluated through a final project and its corresponding written report and oral presentation.
Basic understanding of multivariate data analysis and machine learning techniques.