ECTS credits ECTS credits: 3
ECTS Hours Rules/Memories Hours of tutorials: 4.5 Expository Class: 14 Interactive Classroom: 14 Total: 32.5
Use languages Spanish, Galician
Type: Ordinary subject Master’s Degree RD 1393/2007 - 822/2021
Departments: Electronics and Computing
Areas: Languages and Computer Systems
Center Higher Technical Engineering School
Call: Second Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
The course introduces students to machine learning techniques for the development of digital twins in industrial environments. In particular, it aims for students, by the end of the course, to be able to:
- Know and understand the fundamentals of data preprocessing for industrial facilities.
- Employ techniques to clean and preprocess IoT data for machine learning algorithms.
- Data preprocessing in industry.
- Introduction to anomaly detection.
- Feature selection.
- Development of industrial digital twins. Memory-based machine learning models. Continuous learning models.
- Optimization techniques. Adaptation of classical techniques to industrial processes.
Basic bibliography
[1]. Brink, Henrik, Joseph W Richards, and Mark Fetherolf. Real-World Machine-Learning. Shelter Island, NY: Manning, 2017. Print.
[2]. Kostadinov, Simeon. Recurrent Neural Networks with Python Quick Start Guide: Sequential Learning and Language Modeling with TensorFlow. 1st edition. Birmingham; Packt Publishing, 2018. Print.
[3]. Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. Feature selection: A data perspective. ACM computing surveys (CSUR), 2017. 50(6), 1-45.
Complementary bibliography
[1]. Bernico, Michael. Deep Learning Quick Reference: Useful Hacks for Training and Optimizing Deep Neural Networks with TensorFlow and Keras. First edition. Birmingham, England; Packt, 2018. Print.
[2]. Artasanchez, Alberto, and Prateek Joshi. Artificial Intelligence with Python : Your Complete Guide to Building Intelligent Apps Using Python 3.x and TensorFlow 2. Second edition. Birmingham; Packt Publishing, 2020. Print.
[3]. Gomes, H. M., Read, J., Bifet, A., Barddal, J. P., & Gama, J. Machine learning for streaming data: state of the art, challenges, and opportunities.ACM SIGKDD Explorations Newsletter, 2019, 21(2), 6-22.
The degree program outlines the following competencies for this course:
- I-CP3: Analyze and interpret IoT data streams in the Industry.
The contents of the course will be taught indistinctly between lectures and interactive classes. The completion of all the proposed activities is necessary to pass the course.
Lecture Classes (theory): they will consist of explaining the different sections of the course syllabus, with the help of electronic media (presentations, videos, etc.).
Interactive classes (laboratory): different practical problems related to the content of the subject will be posed for the student to solve individually or in groups.
Autonomous work: the scope and objectives of the projects, use cases and/or practical problems may require autonomous work on the part of the students, albeit under the supervision of the teaching staff.
Office hours: Office hours will be used to solve students' doubts related to the contents of the subject. These office hours can be both face-to-face and virtual (via email, virtual campus or video conferencing platforms). Synchronous office hours will require a prior appointment.
First Call
To pass the course, the student must complete and pass the proposed practical (30%) and supervised (30%) work, which represents 60% of the final grade, as well as pass the final exam, which constitutes the remaining 40%. To do this, it is necessary to obtain a grade equal to or higher than 5 in the overall assessment. Additionally, it is required to achieve at least a 4 in each evaluated part to average.
The final exam questions will focus on the specific content developed in the course in relation to its competencies and may have been acquired by the student in both the lecture and interactive parts.
Mid-term Exams: No mid-term exams will be conducted.
Second call
The qualification obtained in the laboratory part (practical and supervised work) during the course as well as its weight in the final grade are maintained. Students who did not reach the cutoff qualification in the proposed activities during the previous call may submit similar activities to those not passed, which will be proposed by the professors, before the final exam of the second opportunity. Once all the evaluated parts are separately passed, the exam will account for 40% of the final grade, and the laboratory part will constitute the remaining 60% (practical work 30% and supervised work 30%). To pass the course, a global average grade of 5 or higher is necessary. Additionally, it is required to achieve at least a 4 in each evaluated part to average.
The final exam questions will focus on the specific content developed in the course in relation to its competencies and may have been acquired by the student in both the lecture and interactive parts.
Repeating students:
In case of repeating students, they will be examined under the same conditions as students in the first round.
No-show qualification:
The student will receive the qualification of "no-show" when he/she does not take the final exam.
Fraudulent performance of exercises or tests:
In cases of fraudulent conduct in exercises or tests, the official performance evaluation regulations of each institution (USC, UDC, UVigo) will apply. In particular, if any form of plagiarism is detected in any tests or exams, the final grade will be FAIL (0), and the incident will be reported to the appropriate academic authorities.
According to the master's program outline, the course has a workload of 3.0 ECTS. Given that 25 hours are allocated per ECTS, the total workload for the course is 75 hours (3 ECTS x 25 hours per ECTS).
The workload includes 12 hours of lecture classes and 12 hours of laboratory classes. Therefore, personal study time for students should account for 51 hours.
The student should keep up to date with the content subject in order to be able to apply the knowledge acquired in theory in laboratory exercises.
Primary language: the subject will be taught in Spanish.
David Mera Perez
- Department
- Electronics and Computing
- Area
- Languages and Computer Systems
- david.mera [at] usc.es
- Category
- Professor: Temporary PhD professor
| Tuesday | |||
|---|---|---|---|
| 15:30-17:00 | Grupo /CLE_01 | Spanish | Aula A10 |
| 17:00-18:30 | Grupo /CLIL_01 | Spanish | L.B.3. Environmental Engineering Laboratory |