GUÍA DOCENTE

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:

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.

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.
Specific programme competences	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:

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

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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