Degree | Type | Year | Semester |
---|---|---|---|
2503740 Computational Mathematics and Data Analytics | OT | 4 | 1 |
You can check it through this link. To consult the language you will need to enter the CODE of the subject. Please note that this information is provisional until 30 November 2023.
Students are required to have followed inear algebra, to have familiarity of the geometric notions of previous years, and to have some knowledge of Python.
The first goal is to introduce the topological features of data (namely, shapes and patterns). We shall learn the methodology do release this information, as well as some applications
1 Introducció a la topologia
2 Complexos simplicials i homologia
3 Homologia persistent
4 Vectoritzacions
5 Una aplicació: periodicitat de sèries temporals
6 UMAP
There is a theoretical part (including exercises sessions) and a practical part with computer.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Lectures | 25 | 1 | 1, 12, 2, 11, 3, 4, 13, 10, 9, 7, 8, 6, 14, 5 |
Practices with computer | 24 | 0.96 | 1, 12, 2, 11, 3, 4, 13, 10, 9, 7, 8, 6, 14, 5 |
Type: Supervised | |||
Tutoring and consultations | 10 | 0.4 | 1, 12, 2, 11, 3, 4, 13, 10, 9, 7, 8, 6, 14, 5 |
Type: Autonomous | |||
Independent study and preparation | 46 | 1.84 | 1, 12, 2, 11, 3, 4, 13, 10, 9, 7, 8, 6, 14, 5 |
Use of sorftware | 30 | 1.2 | 1, 12, 2, 11, 3, 4, 13, 10, 9, 7, 8, 6, 14, 5 |
Evaluations is organized as follows:
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Continued evaluation practices | 40 | 10 | 0.4 | 1, 12, 2, 11, 3, 4, 13, 10, 9, 7, 8, 6, 14, 5 |
First partial test theory | 30 | 2.5 | 0.1 | 1, 12, 2, 11, 3, 4, 13, 10, 9, 7, 8, 6, 14, 5 |
Presentació final de curs | 30 | 2.5 | 0.1 | 1, 12, 2, 11, 3, 13, 10, 9, 7, 8, 6, 14, 5 |
Compùter practical sessions shall be in Python. We shall use giotto-tda, built on top of scikit-learn