How should intelligence be modelled? There does seem to be a general agreement within the Cognitive Sciences (Psychology, Neuroscience, Artificial Intelligence) that intelligence is computation. However 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 discus 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. Web:http://postgrau.upc.edu/ai/gimaster/courses/minds-brains-and-machines-ub Mail:
Alfredo Vellido Alcacena (
Ruth De Diego (
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
CEA3 - Capability to understand the basic operation principles of Machine Learning main techniques, and to know how to use on 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 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.
CEP5 - Capability to design new tools and new techniques of Artificial Intelligence in professional practice.
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.
Solvent use of the information resources
CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.
Appropiate 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 lack of information.
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..
Analisis y sintesis
CT7 - Capability to analyze and solve complex technical problems.
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.
Understanding some Neuroscience basics
Understanding some Neuroimaging basics as a basis for Neuroscience
Understanding some basics of Computational Neuroscience
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
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
Students are expected to have at least some basic background in the area of artificial intelligence and, more specifically, with the areas of Machine Leaning 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
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