Degree | Type | Year | Semester |
---|---|---|---|
2503852 Applied Statistics | OB | 3 | 2 |
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.
The first year subjects, in addition to Numerical Methods and Optimization and Machine Learning 1.
To learn at theoretical and practical level the potentialities of deep learning for structured and also unstructured data.
Topic 1: Fully connected neural networks.
Topic 2: Convolutional neural networks.
Topic 3: Recurrent neural networks.
Topic 4: Other types of neural networks.
Teaching will combine classroom lessons by teachers and practical work for students with a computer.
In all aspects of teaching/learning activities, the best efforts will be made by teachers and students to avoid language and situations that can be interpreted as sexist.
To achieve continuous improvement in this subject, everyone should collaborate in highlighting them.
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 | |||
Theoretical and practical classes | 50 | 2 | |
Type: Autonomous | |||
Personal study of the subject | 46 | 1.84 | |
Practices | 30 | 1.2 |
Continuous grading
The grading for the course will be done in two parts: the theory part, NT, and the practice part, NP. The final grade for the course will be N = 0.5*NT + 0.5*NP..
The grading fot the theory part will be based in two exams: a partial examn, NEP, and a final exam, NEF. The final grade for the theory part will be NT = max(NEF, 0.3*NEP + 0.7*NEF).
The grading for the practice part will have two parts: una continuous evaluation part, NPC, and one final project part, NPT. The final grade for the practice part will be NP = 0.5*NPC + 0.5*NPT.
On the day of the second-chance exam only the grade for the theory part will be updated. If a student goes to the second-chance examn then the theory grade, NT, will be the grade for the second-chance exam.
In order for an activity to be taken into account in the final grade, the activity grade has to be a minimum of 3,5.
The student who has submitted works for at least 50% of the subject will be considered evaluable. Otherwise, it will appear in the record as non-evaluable.
The grades of the second-chance exam will not be taken into account for the allocation of Honor Mentions.
Single grading
The grading for a student who chooses to be evaluated with the single grading modality will be N = 0.5*NEF+ 0.5*NPT.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Exam | 50% | 4 | 0.16 | 1, 11, 2, 5, 3, 17, 6, 7, 8, 10, 15, 14, 12, 13, 18, 9, 16 |
Task | 50% | 20 | 0.8 | 1, 5, 17, 8, 10, 4, 15, 14, 12, 13, 9, 16 |
Python