Program Overview

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University Programs

The university offers a range of programs for students, including bachelor's degrees, master's degrees, and integrated bachelor-master degrees.

Bachelor's Degrees

• Bachelor Degree in Informatics Engineering
  • Enrolment: Available places
  • Curriculum: Syllabus, Reassessment, Specializations, Competences, Competences for degree subjects
  • Faculty
  • Bachelor's Thesis
  • Timetables
  • Exams
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• Bachelor Degree in Data Science and Engineering
  • Enrolment: Available places
  • Curriculum: Syllabus, Competences, Competences for degree subjects
  • Faculty
  • Timetables
  • Exams
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• Bachelor Degree in Artificial Intelligence
  • Enrolment: Places lliures
  • Curriculum: Competences, Syllabus, Competences for degree subjects
  • Faculty
  • Timetables
  • Exams
  • Academic regulations
  • Bachelor's thesis
• Bachelor Degree in Bioinformatics
  • Enrolment: Available places
  • Curriculum: Learning Outcomes, Syllabus
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• Integrated Bachelor Master Degree
  • Enrolment
  • Curriculum

Master's Degrees

• Master in Informatics Engineering
  • Enrolment: Available places
  • Curriculum: Syllabus, Competences, Competences for degree subjects
  • Faculty
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  • Master's Thesis
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• Master in Informatics Engineering - Industrial Modality
  • Curriculum
• Master in Innovation and Research in Informatics
  • Enrolment: Available places
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  • Master's Thesis
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• Master in Artificial Intelligence
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  • Curriculum: Syllabus, Competences, Competences for degree subjects
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  • Master's Thesis
  • Timetables
  • Exams
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• Master in Cybersecurity
• Master in Data Science
  • Enrolment: Available places
  • Curriculum: Syllabus, Competences, Competences for degree subjects
  • Faculty
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  • Timetables
  • Exams
  • Master's Thesis
    • Gender Competency
• Erasmus Mundus Master in Big Data Management and Analytics
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  • Exams
• Master in Urban Mobility
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• EUMaster4HPC
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• Other Masters
  • Master in Pure and Applied Logic
  • Master in Computational Modelling in Physics, Chemistry and Biochemistry

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Minds, Brains and Machines

Description

How should intelligence be modelled? There seems to be a general agreement within the Cognitive Sciences (Psychology, Neuroscience, Artificial Intelligence) that intelligence is mostly computation. Despite this agreement, these disciplines differ on the adequate level of explanation in which computation should be characterized. Computational Neuroscience, for example, attempts to understand how brains "compute" but it emphasizes descriptions of biologically realistic neurons and their physiology. But, is this an adequate level of explanation?
The aims of the course are to discuss these issues and to briefly introduce AI students in the fields of computational neuroscience, neuroscience, and psychology to see how these disciplines can enrich each other.

Credits

4

Types

Elective

Requirements

This subject has no requirements, but it has previous capacities.

Department

CS;UB

Teachers

• Person in charge:
  • Alfredo Vellido Alcacena
  • Ruth De Diego Balaguer
• Others:
  • Ignasi Cos Aguilera

Weekly hours

• Theory: 2.5
• Problems: 0
• Laboratory: 0
• Guided learning: 0
• Autonomous learning: 5.5

Competences

Generic Technical Competences

• CG1: Capability to plan, design, and implement products, processes, services, and facilities in all areas of Artificial Intelligence.

Technical Competences of each Specialization

Academic

• CEA3: Capability to understand the basic operation principles of Machine Learning main techniques, and to know how to use them in the environment of an intelligent system or service.
• CEA4: Capability to understand the basic operation principles of Computational Intelligence main techniques, and to know how to use them in the environment of an intelligent system or service.
• CEA8: Capability to research in new techniques, methodologies, architectures, services, or systems in the area of Artificial Intelligence.
• CEA11: Capability to understand the advanced techniques of Computational Intelligence, and to know how to design, implement, and apply these techniques in the development of intelligent applications, services, or systems.

Professional

• CEP5: Capability to design new tools and new techniques of Artificial Intelligence in professional practice.

Transversal Competences

Teamwork

• CT3: Ability to work as a member of an interdisciplinary team, as a normal member or performing direction tasks, in order to develop projects with pragmatism and sense of responsibility, making commitments taking into account the available resources.

Information literacy

• CT4: Capacity for managing the acquisition, the structuring, analysis, and visualization of data and information in the field of specialization, and for critically assessing the results of this management.

Appropriate attitude towards work

• CT5: Capability to be motivated for professional development, to meet new challenges, and for continuous improvement. Capability to work in situations with a lack of information.

Reasoning

• CT6: Capability to evaluate and analyze on a reasoned and critical way about situations, projects, proposals, reports, and scientific-technical surveys. Capability to argue the reasons that explain or justify such situations, proposals, etc.

Analysis and synthesis

• CT7: Capability to analyze and solve complex technical problems.

Basic

• CB6: Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.
• CB8: Capability to communicate their conclusions, and the knowledge and rationale underpinning these, to both skilled and unskilled public in a clear and unambiguous way.
• CB9: Possession of the learning skills that enable the students to continue studying in a way that will be mainly self-directed or autonomous.

Objectives

1. Understanding some Neuroscience basics
2. Understanding some Neuroimaging basics as a basis for Neuroscience
3. Understanding some basics of Computational Neuroscience
4. Application of Machine Learning and Computational Intelligence to Computational Neuroscience
5. Reward processing as a Computational Neuroscience problem
6. Computational Neuroscience of vision

Contents

1. Basic concepts of brain function
2. Introduction to Neuroimage Techniques in Neuroscience
3. Brain functions in brain networks and their connectivity
4. Basics of Computational Intelligence
5. Decoding neurocognitive states with neural networks
6. Reward processing and reinforcement learning
7. Computational Intelligence of Vision

Activities

• Essay on a topic of Computational Neuroscience
• Basic concepts of brain function
• Introduction to Neuroimage Techniques in Neuroscience
• Brain functions in brain networks and their connectivity
• Basics of Computational Intelligence
• Decoding neurocognitive states with neural networks
• Reward processing and reinforcement learning
• Computational Intelligence of Vision

Teaching Methodology

This course will build on different teaching methodology (TM) aspects, including:

• TM1: Expositive seminars
• TM2: Expositive-participative seminars
• TM3: Orientation for individual assignments (essays)
• TM4: Individual tutorization

Evaluation Methodology

The course will be evaluated through a final essay that will take one of these three modalities:

1. State of the art on an specific IDA-DM topic
2. Evaluation of an IDA-DM software tool with original experiments
3. Pure research essay, with original experimental content

Bibliography

• The computational brain - Churchland, P.S.; Sejnowski, T.J, The MIT Press, 1992.
• Theoretical neuroscience: computational and mathematical modeling of neural systems - Dayan, P.; Abbott. L.F, The MIT Press, 2001.
• Handbook of functional neuroimaging of cognition - Cabeza, R.; Kingstone, A. (eds.), The MIT Press, 2005.
• Computational maps in the visual cortex - Miikkulainen, R. [et al.], Springer, 2005.

Previous Capacities

Students are expected to have at least some basic background in the area of artificial intelligence and, more specifically, with the areas of Machine Learning and Computational Intelligence. Some basic knowledge of probability theory and statistics, as well as neuroscience, would be beneficial, but not essential. Other than this, the course is open to students and researchers of all types of background.