![]() | Guía Docente 2024-25 SISTEMAS INTELIGENTES |
BASIC DETAILS:
Subject: | SISTEMAS INTELIGENTES | ||
Id.: | 30064 | ||
Programme: | GRADUADO EN INGENIERÍA INFORMÁTICA. PLAN 2008 (BOE 15/12/2008) | ||
Module: | GESTION DE LA INFORMACION Y EL CONOCIMIENTO | ||
Subject type: | OBLIGATORIA | ||
Year: | 3 | Teaching period: | Primer Cuatrimestre |
Credits: | 6 | Total hours: | 150 |
Classroom activities: | 64 | Individual study: | 86 |
Main teaching language: | Inglés | Secondary teaching language: | Castellano |
Lecturer: | Email: |
PRESENTATION:
Professionals capable of designing and building this systems are highly appreciated in modern corporations. This course is an introduction to the methods and algorithms for developing intelligent software systems, with a focus in information representation and reasoning. We will review several topics originated in the Artificial Intelligence field, like datamining and machine learning, problem-solving techniques (graph search, heuristics), and in the field of Knowledge Engineering, like reasoning using Predicate Calculus or with uncertainty. Theory will be complemented with programming assignments in Python, Java, RapidMiner, R ....This course builds on the knowledge and competences acquired by the student in previous courses like Discrete Mathematics, Programming Fundamentals,Algorithms and Data Structures, and Information Systems.
PROFESSIONAL COMPETENCES ACQUIRED IN THE SUBJECT:
General programme competences | G13 | Capacity to use individual learning strategies aimed at continuous improvement in professional life and to begin further studies independently. |
G14 | Capacity for abstraction to handle various complex knowledge models and apply them to examining and solving problems. | |
G15 | Capacity to structure reality by means of linking objects, situations and concepts through logical mathematical reasoning. | |
Specific programme competences | E02 | Capacity to apply the intrinsic engineering principles based on mathematics and a combination of scientific disciplines. |
E03 | Capacity to recognise the technical principles and apply the appropriate practical methods satisfactorily to analyse and solve engineering problems. | |
E12 | Capacity to manage complexity through abstraction, modelling, 'best practices', patterns, standards and the use of the appropriate tools. | |
Learning outcomes | R1 | Present knowledge using various methodologies. |
R2 | Design and construct algorithms for automatic reasoning. | |
R3 | Identify the 'difficult' problems and formulate some adequate strategies using 'intelligent' methods and techniques. | |
R4 | Read and understand the basic Intelligent Systems bibliography. |
PRE-REQUISITES:
SUBJECT PROGRAMME:
Subject contents:
1 - Artificial Intelligent Introduction |
1.1 - Introduction |
2 - Machine Learning and Data Mining |
2.1 - Introduction to the Information Extraction |
2.2 - Clasification Methods |
2.3 - Clustering Methods |
2.4 - Deep Learning |
3 - Knowledge Representation |
3.1 - Introduction to the Expert Systems |
3.2 - Logic and Knowledge Representation |
3.3 - Ontologies |
3.4 - Applications |
4 - Search and Planning |
4.1 - Graph Search |
4.2 - Heuristic Search |
4.3 - Applications |
Subject planning could be modified due unforeseen circumstances (group performance, availability of resources, changes to academic calendar etc.) and should not, therefore, be considered to be definitive.
TEACHING AND LEARNING METHODOLOGIES AND ACTIVITIES:
Teaching and learning methodologies and activities applied:
Student work load:
Teaching mode | Teaching methods | Estimated hours |
Classroom activities | ||
Master classes | 15 | |
Practical work, exercises, problem-solving etc. | 15 | |
Debates | 9 | |
Workshops | 10 | |
Laboratory practice | 10 | |
Assessment activities | 5 | |
Individual study | ||
Tutorials | 2 | |
Individual study | 20,5 | |
Individual coursework preparation | 22 | |
Project work | 31,5 | |
Compulsory reading | 10 | |
Total hours: | 150 |
ASSESSMENT SCHEME:
Calculation of final mark:
Written tests: | 25 | % |
Individual coursework: | 10 | % |
Group coursework: | 25 | % |
Final exam: | 30 | % |
Participation: | 10 | % |
TOTAL | 100 | % |
*Las observaciones específicas sobre el sistema de evaluación serán comunicadas por escrito a los alumnos al inicio de la materia.
BIBLIOGRAPHY AND DOCUMENTATION:
Basic bibliography:
Bonifacio Martín del Frio, Alfredo Sanz Molina, Redes neuronales y sistemas difusos, Alfaomega, 2002. |
Dean Allemang and Jim Hendler, Morgan Kaufmann Semantic Web for the Working Ontologist (2nd ed.), 2011, |
Drew Conway, John Myles White. Machine Learning for Hackers O\\\'Reilly Media February 2012 |
RUSSELL, S; NORVIG, P. Artificial Intelligence: A Modern Approach, 2nd ed. Prentice Hall, 2003 |
Recommended bibliography:
Recommended websites:
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