Degree | Type | Year | Semester |
---|---|---|---|
2503740 Computational Mathematics and Data Analytics | OB | 3 | 2 |
To have completed the subjects of Materia 7: Artificial Intelligence, Machine Learning, and the subjects of Modelling and Inference (2nd), Complex Data Analysis (2nd), and Information Theory (3rd).
This subject aims to give a practical introduction to neural network models and deep learning.
The students will consolidate and extend their theoretical background, by building on top of previous subjects on machine learning and complementing previous knowledge with new concepts on neural network design, deep learning frameworks, and the training process for such models.
The students should finish this subject, having a broad knowledge of different neural network architectures and their typical use scenarios, and a demonstrated capacity to critically choose the right architecture and training mechanisms for each task.
Finally, the students will receive hands-on training and acquire practical experience on using current deep learning frameworks to solve specific tasks.
Neural network design is guided by the types of problems that it aims to solve. Throughout this subject it will be that typology of problems that will provide the motivation of each section and will direct the organization of the contents.
There will be two types of sessions:
Theory classes: The objective of these sessions is for the teacher to explain the theoretical background of the subject. For each one of the topics studied, the theory and mathematical formulation is explained, as well as the corresponding algorithmic solutions.
Laboratory sessions: Laboratory sessions aim to facilitate interaction, collaborative work and to reinforce the comprehension of the topics seen in the theory classes. During laboratory sessions the students will work through practical cases that require the design of solutions using the methods studied in theory classes. Problem solving will be initiated in the class and will be complemented by a weekly set of problems to work through at home.
The above activities will be complemented by a system of tutoring and consultations outside class hours.
All the information of the subject and the related documents that the students need will be available at the virtual campus (cv.uab.cat).
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 | |||
Practical session | 22 | 0.88 | 8, 2, 7, 1, 6, 5, 4, 10, 3 |
Theory session | 28 | 1.12 | 8, 2, 7, 1, 6 |
Type: Supervised | |||
Tutoring | 5 | 0.2 | 9, 8, 2, 7, 1, 3 |
Type: Autonomous | |||
Dedication to practical work | 45 | 1.8 | 9, 8, 2, 7, 6, 5, 4, 3 |
Reading and study of material | 45 | 1.8 | 8, 2, 7, 1, 3 |
To assess the level of student learning, a formula is established that combines theoretical and practical knowledge acquisition, and the ability to solve problems.
The final grade is calculated weighted in the following way and according to the different activities that are carried out:
Final grade = 0.5 * Theory Grade + 0.1 * Problems Portfolio Grade + 0.4 * Practical Evaluation
This formula will be applied as long as the theory and the practical evaluation grades are higher than 5. There is no restriction on the problems portfolio grade. If doing the calculation of the formula yields >= 5 but the theory grade or practical evaluation grade does not reach the minimum required, then a final grade of 4.5 will be given.
The theory grade aims to assess the individual abilities of the student in terms of the theoretical content of the subject, this is done continuously during the course through two partial exams. The overall theory grade is the average of the grades of the two partial exams.
The partial exam #1 is done in the middle of the semester and serves to eliminate part of the subject if it is passed. The partial exam #2 is done at the end of the semester and serves to eliminate part of the subject if it is passed.
These exams aim to assess the abilities of each student in an individualized manner, both in terms of solving problems using the techniques explained in class, as well as evaluating the level of conceptualization that the student has made of the techniques seen. In order to obtain a final pass theory grade, it will be required for the both partial exam grades to be higher than 4. If doing the calculation of the formula yields >= 5 but the grades of any of the two partial exams do not reach the minimum required, then the grade for theory that will be used in the final calculation will be 4.5.
Recovery exam. In case the theory grade does not reach the adequate level to pass, the students can take a recovery exam, destined to recover the failed part (1, 2 or both) of the continuous evaluation process.
The aim of the problems (exercises) is for the student to train with the contents of the subject continuously and become familiar with the application of the theoretical concepts. As evidence of this work, the presentation of a portfolio is requested in which the exercises worked out will be collated. To obtain a problems grade it is required that the student submits a minimum of 70% of the problem sets, in the opposite case, the problems grade will be 0.
The practical abilities of the students will be evaluated twice during the semester. The evaluation will be based on the students presenting and explaining the practical work they have individually completed at home.
The overall practical evaluation grade is the average of the two practical evaluations.
In case of not passing any of the practical evaluations, the student will be able to recover the failed part, restricted to a maximum grade of 7/10.
Notwithstanding other disciplinary measures deemed appropriate, and in accordance with the academic regulations in force, evaluation activities will be suspended with zero (0) whenever a student commits any academic irregularities that may alter such evaluation (for example, plagiarizing, copying, letting copy, ...). The evaluation activities qualified in this way and by this procedure will not be recoverable. If you need to pass any of these assessment activities to pass the subject, this subject will be failed directly, without opportunity to recover it in the same course.
In case there the student does not deliver any exercise solutions, does not participate in any practical evaluation, and does not take any exam, the corresponding grade will be a "non-evaluable". In any other case, the “no shows” count as a 0 for the calculation of the weighted average.
In order to pass the course with honours, the final grade obtained must be equal or higher than 9 points. Because the number of students with this distinction cannot exceed 5% of the total number of students enrolled in the course, it is given to whoever has the highest final marks. In case of a tie, the results of the partial exams will be taken into account.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Examinations | 50 | 4.5 | 0.18 | 8, 2, 7, 1, 6, 4, 3 |
Practicals Evaluation | 40 | 0.5 | 0.02 | 9, 8, 2, 7, 6, 5, 4, 10, 3 |
Problems deliverables | 10 | 0 | 0 | 8, 2, 7, 1, 6, 5, 4, 3 |
Books:
Books online
For the practical activities of the course we will use Python (NumPy, MatPlotLib, SciKit Learn) and PyTorch