Guía Docente 2020-21


Id.: 33294
Programme: GRADUADO EN BIOINFORMÁTICA. PLAN 2019 (BOE 06/02/2019)
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:


This course provides an introduction on artificial intelligence techniques for bioinformatics. It gives an overview of basic concepts, techniques and algorithms in machine learning, knowledge representation and reasoning, and planning. The course will draw from numerous case studies and applications, so that students will learn how to apply these techniques to relevant bioinformatics problems.


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.


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 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 applied:

Class sessions: during this course a variety of teaching methods will be used including lectures, exercises and laboratory sessions. It is important that students participate actively in class, either in the classroom or through the online platform.
Tutorials: During these sessions, students can ask questions, clarify concepts, ask for additional feedback or bibliography either face to face or electronically. 
Independent Study: Students are expected to complete all independent study tasks, which will be uploaded on the PDU regularly. Students are required to upload their completed tasks on the PDU before the deadline. It is therefore important that students check the PDU every week. All tasks must be completed in English.

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


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.


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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