Checking date: 26/03/2019


Course: 2019/2020

Intelligence in Networks
(15403)
Study: Bachelor in Telecommunication Technologies Engineering (252)


Coordinating teacher: VILLENA ROMAN, JULIO

Department assigned to the subject: Department of Telematic Engineering

Type: Electives
ECTS Credits: 6.0 ECTS

Course:
Semester:




Students are expected to have completed
Programming skills (covered in "Programming" and "Systems Programming" in 1st year), knowledge of machine learning algorithms and an interest in Artificial Intelligent.
Competences and skills that will be acquired and learning results. Further information on this link
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 (AI), 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 AI - 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
Description of contents: programme
1. Concepts and history of Artificial Intelligence 2. Problem solving and search strategies 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 Software Agents and Distributed Artificial Intelligence 4. Data Science 4.1. Concepts of Data Science/Analytics/Mining 4.2. Learning and knowledge adquisition 5. Linguistic Engineering (Natural Language Processing) and Semantic Web
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 (3 ECTS credits) - 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 (1 ECTS credit) - 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 (2 ECTS credits)
Assessment System
  • % end-of-term-examination 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60
Basic Bibliography
  • Fernández, Gregorio. Representación del conocimiento en sistemas inteligentes. online: http://www.gsi.dit.upm.es/~gfer/ssii/rcsi/. 2004
  • 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 *
Additional Bibliography
  • Mira, J.; Delgado, A.; Sánchez Boticario, J.. Aspectos básicos de la Inteligencia Artificial. Ed. Sanz y Torres. 1995
  • Nils J. Nilsson. Inteligencia artificial: una nueva síntesis. McGraw-Hill. 2000
  • P. Adriaans, P.; Zantinge, D.. Data Mining. Addison-Wesley. 1996
  • Piatetsky-Shapiro G., Frawley J. (eds.). Knowledge Discovery in Databases. MIT Press. 1991
  • Rich, E.; Knight, K. . Artificial Intelligence. McGraw-Hill. 1994
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN


The course syllabus and the academic weekly planning may change due academic events or other reasons.


More information: http://www.it.uc3m.es/jvillena/inred