Checking date: 03/07/2020

Course: 2020/2021

Intelligent data processing tools
Study: Master in Connected Industry 4.0 (357)

Coordinating teacher: MUÑOZ ORGANERO, MARIO

Department assigned to the subject: Department of Telematic Engineering

Type: Compulsory
ECTS Credits: 3.0 ECTS


Students are expected to have completed
This subject is based on the knowledge given in the previous subject of the same Masters' programme: - Data analytics in IC4.0
Competences and skills that will be acquired and learning results.
BASIC COMPETENCES CB6 Possess and understand knowledge that provides a basis or opportunity to be original in the development and / or application of ideas, often in a research context CB7 That students know how to apply the knowledge acquired and their ability to solve problems in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their area of study CB10 That students have the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous GENERAL COMPETENCES CG3 Capacity to develop basic distributed applications for the transport, storage and management of information. CG5 Capacity for basic analysis of the requirements for information management and treatment of large volumes of data. CG6 Capacity to adapt to changes in requirements associated with new products, new specifications and environments. SPECIFIC COMPETENCES CE10 Programmatic data processing capabilities in solving particular problems of the connected industry LEARNING RESULTS As a result of the learning the student will be able to: - Collect and store data including the cloud as support. - Perform advanced statistical processing.
Description of contents: programme
- Pre-processing of data: Techniques, methods, tools and applications for detection of outliers - Programming and tools for data analysis - Analysis of data in the cloud - Advanced classification methods with SVM and shallow neural networks - Advanced classification and regression with deep learning methods. Restricted Boltzmann machines and Autoencoders. - Advanced classification and regression with deep learning methods. Deep recurrent neural networks. - Data management in the cloud. Tools and architectures.
Learning activities and methodology
LEARNING ACTIVITIES AF1 Theoretical class AF2 Practical classes AF4 Laboratory practices AF5 Tutorials AF6 Group work AF7 Individual student work AF8 Partial and final exams Code No. No. Activity Total hours Face to face hours % Presence Student AF1 12 12 100 AF2 6 6 100 AF4 3 3 100 AF5 2 2 100 AF6 25 0 0 AF7 25 0 0 AF8 2 2 100 TOTAL 75 25 33% EDUCATIONAL TRAINING METHODOLOGIES MD1 Exhibitions in the teacher's class with support of computer and audiovisual media, in which the main concepts of the subject are developed and the bibliography is provided to complement the students' learning. MD2 Critical reading of texts recommended by the teacher of the subject: articles, reports, manuals and / or academic articles, either for further discussion in class, or to expand and consolidate the knowledge of the subject. MD3 Resolution of practical cases, problems, etc. raised by the teacher individually or in groups MD4 Exhibition and discussion in class, under the teacher's moderation of topics related to the content of the subject, as well as case studies
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Basic Bibliography
  • Aurelien Geron . Hands-On Machine Learning with Scikit-Learn and TensorFlow. O'Reilly. 2017
  • Aurelien Geron . Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent . O'Reilly. 2019
  • Charu C. Aggarwal. Neural Networks and Deep Learning: A Textbook. Springer. 2018
  • Sebastian Raschka y Vahid Mirjalili . Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition. Packt. 2017
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • Francois Chollet. Deep Learning with Python. Manning. 2017
Recursos electrónicosElectronic Resources *
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The course syllabus and the academic weekly planning may change due academic events or other reasons.

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