COURSE DESCRIPTION ADVANCED PATTERN RECOGNITION TECHNIQUES YEAR 1 SEMESTER 1 MASTER IN BIOMEDICAL ENGINEERING MODALITY: ON CAMPUS ACADEMIC YEAR ...
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COURSE DESCRIPTION ADVANCED PATTERN RECOGNITION TECHNIQUES YEAR 1 SEMESTER 1 MASTER IN BIOMEDICAL ENGINEERING MODALITY: ON CAMPUS ACADEMIC YEAR 2020/2021 POLYTECHNIC SCHOOL
Advanced Pattern Recognition Techniques / 2020-2021
1. COURSE/SUBJECT IDENTIFICATION
1.- COURSE/SUBJECT:
Name: Advanced Pattern Recognition Techniques
Code:
Year (s) course is taught: 1 Semester(s) when the course is taught: 1st
Type: Compulsory subject ECTS of the course: 5 Hours ECTS= 30
Language: English Type of course: On campus
Degree (s) in which the course is taught: Biomedical Engineering
School in which the course is taught: EPS
2.- ORGANIZATION OF THE COURSE:
Department: Information technology
Area of knowledge: Biomedical engineering
2. LECTURERS OF THE COURSE/SUBJECT
1.-LECTURERS:
Responsible of the Course CONTACT
Name: Abraham Otero Quintana
Phone (ext): 913724046 (14649)
Email: aotero@ceu.es
Office: D 2.6.4
Teaching and Research profile Associate Professor of computer science and artificial
intelligence
Research Lines Analysis of physiological parameters; bioinformatics.
2.- TUTORIALS:
For any queries students can contact lecturers by e-mail, phone or visiting their office during the
teacher’s tutorial times published on the students’ Virtual Campus.
3. COURSE DESCRIPTION
This course presents the techniques most commonly employed in the analysis of large volumes of data, in
the extraction of knowledge from this data, and in making decisions based on the knowledge acquired.
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4. COMPETENCIES
1.- COMPETENCIES
Código de la
Competencias Básicas
competencia
Poseer y comprender conocimientos que aporten una base u oportunidad de ser
CB6 originales en el desarrollo y/o aplicación de ideas, a menudo en un contexto de
investigación.
Que los estudiantes sepan aplicar los conocimientos adquiridos y su capacidad de
CB7 resolución de problemas en entornos nuevos o poco conocidos dentro de contextos
más amplios (o multidisciplinares) relacionados con su área de estudio.
Que los estudiantes sean capaces de integrar conocimientos y enfrentarse a la
complejidad de formular juicios a partir de una información que, siendo incompleta o
CB8
limitada, incluya reflexiones sobre las responsabilidades sociales y éticas vinculadas a
la aplicación de sus conocimientos y juicios.
Que los estudiantes sepan comunicar sus conclusiones y los conocimientos y razones
CB9 últimas que las sustentan a públicos especializados y no especializados de un modo
claro y sin ambigüedades.
Que los estudiantes posean las habilidades de aprendizaje que les permitan continuar
CB10
estudiando de un modo que habrá de ser en gran medida auto dirigido o autónomo.
Código de la
Competencias generales
competencia
CG1 Aplicar el pensamiento analítico.
CG2 Ofrecer soluciones innovadoras a los problemas planteados.
Código de la
Competencias específicas
competencia
Aplicar herramientas avanzadas de la ingeniería, las matemáticas y la física en la
CE01
resolución de problemas biomédicos.
Aplicar técnicas de aprendizaje automático para la extracción de patrones y
CE03
conocimiento a partir de datos médicos.
Diseñar sistemas de gestión de información hospitalarios, incluyendo soluciones de
CE04 eHealth y de mHealth, conociendo los estándares que permiten la interoperabilidad de
dichos sistemas.
Comprender los requerimientos legales aplicados al almacenamiento y al tratamiento
CE07
de datos biomédicos.
2.- LEARNING OUTCOMES:
Code Learning outcomes
RA-1 Collect, integrate and biomedical data preprocessing for building a data warehouse.
RA-2 Ability to graph data and to preprocess data.
RA-3 Discover, interpret and evaluate patterns (knowledge) using data mining techniques.
RA-4 Knowing, understanding the ethical and legal implications of Data mining.
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5. LEARNING ACTIVITIES
1.- DISTRIBUTION OF STUDENTS` ASSIGNMENT:
Total hours of the course 150
DESCRIPTION OF LEARNING ACTIVITIES:
Code Name On-campus
hours
AF1 Lecture 25
AF2 Seminar of exercises-problems 10
AF3 Practice 15
TOTAL Presence Hours 50
Code Name Not on-
campus hours
AF5 Self student work 100
2.- DESCRIPTION OF LEARNING ACTIVITIES:
Activity Definition
AF1 Lecture Learning activity oriented preferably to the competence of
acquisition of knowledge and representative of more
theoretical subjects. This activity gives priority to the
transmission of knowledge by the professor, with the
previous preparation or later study from the student.
AF2 Seminar of exercises-problems Learning activity oriented preferably to the competence of
application of knowledge (competence 2 MECES) and
representative of subjects or practical activities (labs, radio
studies, TV studies and/or any other proper space).
AF3 Practice Training activity involving appropriate laboratory material
and, under the guidance of the teacher-tutor, fosters
autonomous and / or cooperative learning of the student
through the technical realization of practices or projects.
AF5 Self student work Training activity which consists on the autonomous student
learning outside the class environment.
6. ASSESMENT OF LEARNING
1.- CLASS ATTENDANCE:
Class attendance is recorded on the student portal but is not evaluated. Justifications of absence
will not be accepted.
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2.- ASSESMENT SYSTEM AND CRITERIA:
ORDINARY EXAMINATION (continuous assessment)
Percentage
Partial test (S1) 15%
Mid term project (S2) 25%
Final course project (S2) 35%
Final test (S1) 25%
Test Description of the test Approximate weight
SE-1: Written test Written tests. 40%
SE-2: Portfolio Set of physical or digital deliverables 60%
results or parts of a project.
RE-TAKE EXAM/EXTRAORDINARY EXAMINATION
Name Percentage
Final exam 100%
7. COURSE PROGRAMME
1.- COURSE PROGRAMME:
1. Introduction to Pattern Recognition
2. Data warehouses
3. Data preparation
4. Clustering
5. Classifiers
6. Model Evaluation
7. Ethical considerations of data mining in medicine
Practical projects:
1. Analysis of a database using supervised pattern recognition techniques (for example,
decision trees, neural networks, Bayesian networks ...) of data mining, including cleaning,
pre-processing, visualization, model training and validation of the models learned.
2. Analysis of a database using unsupervised pattern recognition techniques (for example,
association rules, clustering, correlation search ...).
8. RECOMMENDED READING
1.- ESSENTIAL BIBLIOGRAPHY:
Data Mining: The Textbook (2015) de Charu C. Aggarwal 978-3-319-141
Data Mining for the Masses, Second Edition: with implementations in RapidMiner and R r.
Matthew North (Autor), Nivedita Bijlani (Redactor), Erica Brauer 0615684378
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9. ATTITUDE IN THE CLASSROOM
1.- REGULATIONS
Any irregular act of academic integrity (no reference to cited sources, plagiarism of work or
inappropriate use of prohibited information during examinations) or signing the attendance
sheet for fellow students not present in class will result in the student not being eligible for
continuous assessment and possibly being penalized according to the University regulations.
10. EXCEPTIONAL MEASURES
Should an exceptional situation occur which prevents continuing with face-to-face teaching
under the conditions previously established to this end, the University will take appropriate
decisions and adopt the necessary measures to guarantee the acquisition of skills and
attainment of learning outcomes as established in this Course Unit Guide. This will be done in
accordance with the teaching coordination mechanisms included in the Internal Quality
Assurance System of each degree.
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