| Competencies / Study results |
|
Code
|
Study programme competences / results
|
| A11 |
CE10 - Ability to implement, validate and apply a stochastic model starting from the observed data on a real system, and to perform a critical analysis of the obtained results, selecting those ones most suitable for problem solving |
| A12 |
CE11 - Understanding and command of the main techniques and tools for data analysis, both from the statistical and the machine learning viewpoints, including those devised for large volumes of data, and ability to select those ones most suitable for problem solving |
| A13 |
CE12 - Ability to outline, formulate and solve all the stages of a data project, including the understanding and command of basic concepts and techniques for information search and filtering in big collections of data |
| A16 |
CE15 - Knowledge of computer tools in the field of machine learning and ability to select those ones most suitable for problem solving |
| B2 |
CG02 - Successfully addressing each and every stage of an AI project |
| B3 |
CG03 - Searching and selecting that useful information required to solve complex problems, with a confident handling of bibliographical sources in the field |
| B4 |
CG04 - Suitably elaborating written essays or motivated arguments, including some point of originality, writing plans, work projects, scientific papers and formulating reasonable hypotheses in the field |
| B5 |
CG05 - Working in teams, especially of multidisciplinary nature, and being skilled in the management of time, people and decision making |
| B6 |
CB01 - Acquiring and understanding knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, frequently in a research context |
| B7 |
CB02 - The students will be able to apply the acquired knowledge and to use their capacity of solving problems in new or poorly explored environments inside wider (or multidisciplinary) contexts related to their field of study |
| B8 |
CB03 - The students will be able to integrate different pieces of knowledge, to face the complexity of formulating opinions (from information that may be incomplete or limited) and to include considerations about social and ethical responsibilities linked to the application of their knowledge and opinions |
| B9 |
CB04 - The students will be able to communicate their conclusions, their premises and their ultimate justifications, both to specialised and non-specialised audiences, using a clear style language, free from ambiguities |
| C3 |
CT03 - Use of the basic tools of Information and Communications Technology (ICT) required for the student's professional practice and learning along her life |
| C4 |
CT04 - Acquiring a personal development for practicing a citizenship under observation of the democratic culture, the human rights and the gender perspective |
| C7 |
CT07 - Developing the ability to work in interdisciplinary or cross-disciplinary teams to provide proposal that contribute to a sustainable environmental, economic, political and social development |
| C8 |
CT08 - Appreciating the importance of research, innovation and technological development in the socioeconomic and cultural progress of society |
| C9 |
CT09 - Being able to manage time and resources: outlining plans, prioritising activities, identifying criticisms, fixing deadlines and sticking to them |
| Learning aims |
| Learning outcomes |
Study programme competences / results |
| Know the techniques that allow the design of scalable AI techniques at software and hardware resources level. |
AC10
AC11
AC12
AC15
|
BC2
BC7
|
CC3
CC4
|
| Acquire the skills to integrate large volume and variety of data in AI Big Data projects. |
AC10
AC11
AC12
AC15
|
BC3
BC4
BC5
BC6
BC7
BC8
BC9
|
CC3
CC4
CC7
CC8
CC9
|
| To know the scalability paradigms in machine learning algorithms. |
AC10
AC11
AC12
AC15
|
BC2
BC3
BC4
BC5
BC6
BC7
BC8
BC9
|
CC3
CC4
CC7
CC8
CC9
|
| Understand, analyze and design the necessary infrastructures for Big Data AI projects: local/cloud environment and physical/virtual equipment with low latency storage systems and distributed file systems |
AC12
AC15
|
BC2
BC6
BC7
BC8
|
CC3
CC4
CC7
CC9
|
| To know the languages, frameworks and components that allow us to increase performance in hardware infrastructures with CPU and GPU. |
AC11
AC15
|
BC3
BC7
BC8
|
CC3
CC4
CC7
CC9
|
| To know the techniques that allow, with low latency, the visualization of data in environments with large volume of information. |
AC11
AC12
AC15
|
BC2
BC3
BC5
BC6
BC7
BC8
BC9
|
CC3
CC4
CC7
CC8
CC9
|
| Use and be able to apply the correct KPIs in each environment. |
AC10
AC11
AC15
|
BC2
BC3
BC7
BC8
|
CC3
CC9
|
| Contents |
| Topic |
Sub-topic |
| Introduction to Big Data |
What is Big Data
Big Data applications
Big Data analytics
Data analysis problems in big data environments |
| Data preparation and visualization |
Data preprocessing techniques
Visualization techniques |
| Federated learning |
Edge learning
Privacy preservation |
| Infrastructures for Big Data storage and processing |
Parallelism and distributed-memory systems
High Performance Computing versus Big Data Computing
Apache Hadoop and MapReduce
|
| Large-scale data processing: Apache Spark |
Batch and streaming processing
Architecture
Spark Core (RDDs) and Spark SQL, DataSets and DataFrames
Spark DataFrames
|
| Machine Learning with Apache Spark |
Machine Learning workflow
Supervised and unsupervised machine learning
Tuning, evaluation and pipelines
|
| Planning |
| Methodologies / tests |
Competencies / Results |
Teaching hours (in-person & virtual) |
Student’s personal work hours |
Total hours |
| ICT practicals |
A11 A12 A13 A16 B2 B3 B4 B5 B6 B7 B8 B9 C3 C7 C8 C9 |
14 |
44 |
58 |
| Objective test |
A11 A12 A13 B2 B6 B7 B8 B9 C4 C8 C9 |
2 |
20 |
22 |
| Collaborative learning |
B3 B4 B5 B6 B8 B9 C4 C7 C8 C9 |
7 |
19 |
26 |
| Guest lecture / keynote speech |
A11 A12 A13 A16 B2 B3 B4 B6 B8 B9 C4 C8 C9 |
21 |
21 |
42 |
| |
| Personalized attention |
|
2 |
0 |
2 |
| |
| (*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
| Methodologies |
|
Methodologies |
Description |
| ICT practicals |
Practical classes in the computer classroom, which allow the student to familiarize himself/herself from a practical point of view with the issues exposed in the theoretical classes. |
| Objective test |
Test in which the student must demonstrate the acquired knowledge from the course |
| Collaborative learning |
Learning based on problems, seminars, case studies or projects, which allow students to acquire certain competences based on the resolution of exercises competencies based on the resolution of exercises, case studies and projects |
| Guest lecture / keynote speech |
Theory classes, in which the content of each topic is exposed. The student will have copies of the transparencies beforehand and the professor will promote an active attitude, asking questions to clarify specific aspects and leaving open questions for the student's reflection |
| Personalized attention |
|
Methodologies
|
| Collaborative learning |
| ICT practicals |
|
| Description |
| Realization of the practical work with the advice of the teacher. Writing documents summarizing the results in the form of reports or articles, as well as the presentation of the results with the teacher or in public sessions within the class. |
|
| Assessment |
|
Methodologies
|
Competencies / Results |
Description
|
Qualification
|
| Collaborative learning |
B3 B4 B5 B6 B8 B9 C4 C7 C8 C9 |
The completion of collaborative learning projects will be evaluated, where students will work (preferably in pairs or groups) to develop a scientific article in detail, related to the topics covered in theory, and present it to the entire class, where questions can be asked. These projects can be completed during non-face-to-face teaching hours, and their objective is to deepen the content of the subject, as well as to acquire competencies in critical analysis, summarization, and oral presentation. The degree of compliance with the specifications, methodology, rigor, and presentation of results will be assessed. |
5 |
| ICT practicals |
A11 A12 A13 A16 B2 B3 B4 B5 B6 B7 B8 B9 C3 C7 C8 C9 |
Assessment of practical work: 50% marks
the solutions proposed by the students to the exposed practices will be evaluated. The internship evaluation can take place
through a correction by the teacher, a defense of the solution provided by the student before the teacher or an oral presentation of the developed solution. All work must be delivered before the dates to be specified and must meet minimum quality requirements to be considered. The degree of compliance with the specifications, the methodology and rigor and the presentation of results will be assessed |
50 |
| Objective test |
A11 A12 A13 B2 B6 B7 B8 B9 C4 C8 C9 |
Questions about the contents of the subject (which can be of the test type or problems to solve), based on the different advanced machine learning techniques and their applications. |
45 |
| |
|
Assessment comments |
To pass the subject, a total score of 5 or higher must be achieved. It is essential to pass all the practices indicated as mandatory. Late submissions will not be assessed Condition for qualification of Not Presented: do not present any practice and do not attend the final exam. Students who are not newly enrolled do not retain grades from previous courses. Recovery opportunity (July) and extraordinary: The assessment will be the same as in the ordinary opportunity. Students who have not submitted the proposed assignments throughout the semester must submit them before the established date. Condition for qualification of Not Presented: do not present any practice and do not attend the final exam. The submitted work must be original by the student. In accordance with article 14, section 4, of the regulations, the delivery of non-original works or with duplicate parts (either by copies between colleagues or by obtaining from other sources...) will carry a global grade of SUSPENSION IN THE ANNUAL CALL, both for the /a student who presents copied material as if to whom it was provided. |
| Sources of information |
|
Basic
|
|
|
- Class notes provided by the professors - A. Polak, <i>Scaling Machine Learning with Spark</i>, O'Reilly, 2023 - I. Triguero, M. Galar, <i>Large-Scale Data Analytics with Python and Spark</i>, Cambridge University Press, 2023 |
|
Complementary
|
|
|
- T. White, <i>Hadoop: The Definitive Guide</i>, 4th Edition, O'Reilly, 2015 - J. Damji, B. Wenig, T. Das and D. Lee. <i>Learning Spark</i>, 2nd Edition, O'Reilly, 2020 |
| Recommendations |
| Subjects that it is recommended to have taken before |
| AI Fundamentals/614544001 | | Machine Learning I /614544012 | | Machine Learning II /614544014 |
|
| Subjects that are recommended to be taken simultaneously |
|
| Subjects that continue the syllabus |
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