![]() | Guía Docente 2024-25 SISTEMAS INTELIGENTES |
BASIC DETAILS:
Subject: | SISTEMAS INTELIGENTES | ||
Id.: | 33294 | ||
Programme: | GRADUADO EN BIOINFORMÁTICA. PLAN 2019 (BOE 06/02/2019) | ||
Module: | BIOINFORMÁTICA | ||
Subject type: | OBLIGATORIA | ||
Year: | 2 | Teaching period: | Segundo Cuatrimestre |
Credits: | 6 | Total hours: | 150 |
Classroom activities: | 63 | Individual study: | 87 |
Main teaching language: | Inglés | Secondary teaching language: | Inglés |
Lecturer: | Email: |
PRESENTATION:
PROFESSIONAL COMPETENCES ACQUIRED IN THE SUBJECT:
General programme competences | G01 | Use learning strategies autonomously for their application in the continuous improvement of professional practice. |
G02 | Perform the analysis and synthesis of problems of their professional activity and apply them in similar environments. | |
G05 | Communicate professional topics in Spanish and / or English both orally and in writing. | |
G06 | Solve complex or unforeseen problems that arise during the professional activity within any type of organisation and adapt to the needs and demands of their professional environment. | |
G07 | Choose between different complex models of knowledge to solve problems. | |
G09 | Apply information and communication technologies in the professional field. | |
G10 | Apply creativity, independence of thought, self-criticism and autonomy in the professional practice. | |
Specific programme competences | E02 | Develop the use and programming of computers, databases and computer programs and their application in bioinformatics. |
E06 | Apply the fundamental principles and basic techniques of intelligent systems and their practical application in the field of bioinformatics. | |
Learning outcomes | R01 | Learn the basics of knowledge representation in artificial intelligence. |
R02 | Apply artificial intelligence algorithms and methods. | |
R03 | Understand planning methods and algorithms | |
R04 | Apply statistical algorithms and methods. | |
R05 | Apply machine learning algorithms and methods. |
PRE-REQUISITES:
This course will be delivered in English. Academic reading and writing skills are expected from students. Also, theory will be complemented with programming examples, so students should be able to understand and write code in R or Python.
SUBJECT PROGRAMME:
Subject contents:
1 - What is artificial intelligence? |
2 - Introduction to machine learning |
3 - Supervised learning |
3.1 - K Nearest Neighbors |
3.2 - Naive Bayes |
3.3 - Neural Networks |
3.4 - Support Vector Machines |
3.5 - Classification Trees and Random Forests |
4 - Performance and meta-learning |
4.1 - Measurements of performance |
4.2 - Bagging and Boosting |
5 - Unsupervised learning |
6 - Planning |
7 - Knowledge representation and rasoning |
7.1 - Logic |
7.2 - Ontologies |
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 | 28 | |
Practical exercises | 4 | |
Practical work, exercises, problem-solving etc. | 10 | |
Coursework presentations | 2 | |
Workshops | 7 | |
Laboratory practice | 4 | |
Other practical activities | 4 | |
Assessment activities | 4 | |
Individual study | ||
Tutorials | 5 | |
Individual study | 27 | |
Individual coursework preparation | 31 | |
Research work | 4 | |
Compulsory reading | 10 | |
Portfolio | 10 | |
Total hours: | 150 |
ASSESSMENT SCHEME:
Calculation of final mark:
Individual coursework: | 5 | % |
Final exam: | 50 | % |
Lab coursework: | 35 | % |
Tests: | 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:
LANTZ, Brett. Machine learning with R. Packt Publishing Ltd, 2019. |
RUSSELL, Stuart J.; NORVIG, Peter. Artificial intelligence: a modern approach. Pearson Education Limited, 2016. |
Recommended bibliography:
Recommended websites:
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