Checking date: 13/06/2023

Course: 2023/2024

Intelligence in Networks
Dual Bachelor Data Science and Engineering - Telecommunication Technologies Engineering (Plan: 456 - Estudio: 371)

Coordinating teacher: CALLEJO PINARDO, PATRICIA

Department assigned to the subject: Telematic Engineering Department

Type: Electives
ECTS Credits: 6.0 ECTS


Requirements (Subjects that are assumed to be known)
Programming skills: - Programming - Systems Programming - Systems Architecture
The main objective of this course is to analize the concept of "intelligence" in information and communication systems and study the main techniques that allow to incorporate "intelligent" behaviours in them. At the end of the course, the students have to study the fundamentals of Artificial Intelligence, the impact of the incorporation of intelligent mechanisms in software and hardware systems and the areas where these technologies may bring the most significant advances. Competences or specific skills that the student must acquire include: - Knowledge of the main concepts and techniques of Artificial Intelligence. - Capacity to analyze the application and feasibility of different AI techniques to solve a specific problem, and evaluate the impact on real-world systems (analysis, abstraction, problem solving and capacity to apply theoretical concepts). In addition, the student will acquire general skills: - Ability to work in teams and share and distribute the work load to deal with complex problems. - The student must learn how to plan the development of a system with a certain degree of complexity. - The student must learn how to search for useful information in different sources for the design and implementation of a given engineering problem.
Skills and learning outcomes
Description of contents: programme
1. Concepts and history of Artificial Intelligence 2. Problem solving and search strategies 2.1. Concepts 2.2. Uninformed search strategies 2.3. Heuristic search strategies 2.4. Game theory 3. Knowledge Based Systems (KBS) and expert systems 3.1. Knowledge representation 3.2. Fundamentals of formal logic, logic programming and inference systems 3.3. Management of uncertaintity 3.4. Agents 4. Machine learning 4.1. Concepts of machine learning, Data Science/Analytics/Mining 4.2. Techniques, tools and applications 4.3. Supervised learning 4.4. Unsupervised learning 4.5. Other 5. Linguistic Engineering (Natural Language Processing)
Learning activities and methodology
Learning activities include: - Theoretical lectures, individual and group tutoring sessions, student presentations, student personal work, including study, tests and exams; focused on the acquisition of the specific coginitive competences of the course - Practical lectures, lab sessions, individual and group tutoring sessions, including study, tests and exams; focused on the development of the specific instrumental competences and most of the general competences, such as analysis, abstraction, problem solving and capacity to apply theoretical concepts - Development and presentation in class of a group project, focused on any of the topics that are included in the course, whose objective is to check that the student is able to develop (design, implement and validate) a software system that includes one or several Artificial Intelligence components to solve a given engineering problem
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Calendar of Continuous assessment
Basic Bibliography
  • Han, J.; Kamber, M. . Data Mining: Concepts and Techniques (2nd Edition). Morgan Kaufmann Publishers. 2006
  • Russell, S.J.; Norvig, P.. Artificial Intelligence. A modern Approach (2nd ed). Prentice-Hall. 2003
  • Witten, Ian H.; Frank, Eibe; Hall, Mark A.. Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition. Morgan Kaufmann. 2011
Recursos electrónicosElectronic Resources *
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The course syllabus may change due academic events or other reasons.