Guía Docente 2023-24 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: | 65 | Individual study: | 85 |
Main teaching language: | Inglés | Secondary teaching language: | Castellano |
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. | |
G03 | Cooperate to achieve common results through teamwork in a context of integration, collaboration and empowerment of critical discussion. | |
G04 | Reason critically based on information, data and lines of action and their application on relevant issues of a social, scientific or ethical nature. | |
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. |
E03 | Apply the fundamental concepts of mathematics, logic, algorithmics and computational complexity to solve problems specific to bioinformatics. | |
E04 | Program applications in a robust, correct, and efficient way, choosing the paradigm and the most appropriate programming languages, applying knowledge about basic algorithmic procedures and using the most appropriate types and data structures. | |
E05 | Implement well-founded applications, previously designed and analysed, in the characteristics of the databases. | |
E06 | Apply the fundamental principles and basic techniques of intelligent systems and their practical application in the field of bioinformatics. | |
E07 | Apply the principles, methodologies and life cycles of software engineering to the development of a project in the field of bioinformatics. | |
E12 | Apply the principles and techniques of protein computational modelling to predict their biological function, their activity or new therapeutic targets (Structural Bioinformatics, Computational Toxicology). | |
E13 | Apply omics technologies for the extraction of statistically significant information and for the creation of relational databases of biodata that can be updated and publicly accessible to the scientific community. | |
E14 | Use programming languages, most commonly used in the field of Life Sciences, to develop and evaluate techniques and/ or computational tools. | |
E15 | Infer the evolutionary history of genes and proteins through the creation and interpretation of phylogenetic trees. | |
E16 | Plan linkage and association studies for medical and environmental purposes. | |
E17 | Induce complex relationships between samples by applying statistical and classification techniques. | |
E18 | Apply statistical and computational methods to solve problems in the fields of molecular biology, genomics, medical research and population genetics. | |
E21 | Apply computational and data processing techniques for the integration of physical, chemical and biological concepts and data for the description and/ or prediction of the activity of a substance in a given context. |
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 |
4 - Performance and meta-learning |
4.1 - Evaluating performance |
4.2 - Improving performance |
5 - Unsupervised learning |
6 - Knowledge representation and reasoning |
7 - Planning |
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 | 6 | |
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 | 2 | |
Compulsory reading | 10 | |
Portfolio | 10 | |
Total hours: | 150 |
ASSESSMENT SCHEME:
Calculation of final mark:
Final exam: | 45 | % |
Lab coursework: | 45 | % |
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:
RUSSELL, Stuart J.; NORVIG, Peter. Artificial intelligence: a modern approach. Pearson Education Limited, 2016. |
LANTZ, Brett. Machine learning with R. Packt Publishing Ltd, 2019. |
Recommended bibliography:
Recommended websites:
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