Minds, Brains and Machines

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Inici » Estudis » Secretaria » Plans Extingits » Màster en intel.ligència artificial - 2012
Crèdits
4
Departament
UB
Tipus
Optatives
Requisits
Aquesta assignatura no té requisits
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:

Professors

Responsable

  • Alfredo Vellido Alcacena ( )
  • Ruth De Diego ( )

Hores setmanals

Teoria
2
Problemes
0.5
Laboratori
0
Aprenentatge dirigit
0.5
Aprenentatge autònom
5.33

Competències

Generic Technical Competences

Generic

  • 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 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.

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.

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.

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..

Analisis y sintesis

  • 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.

Objectius

  1. Understanding some Neuroscience basics
    Related competences: CT4, CB6, CB8, CB9,
  2. Understanding some Neuroimaging basics as a basis for Neuroscience
    Related competences: CT4, CT6, CT7, CB6, CB8, CB9,
  3. Understanding some basics of Computational Neuroscience
    Related competences: CT4, CT5, CT6, CT7, CB6, CB8, CB9,
  4. Application of Machine Learning and Computational Intelligence to Computational Neuroscience
    Related competences: CT3, CT4, CT5, CT6, CT7, CEA3, CEA4, CEP5, CB6, CB8, CB9,
  5. Reward processing as a Computational Neuroscience problem
    Related competences: CT4, CT6, CT7, CB6, CB8, CB9,
  6. Computational Neuroscience of vision
    Related competences: CT3, CT4, CEA8, CEA11, CG1, CB6, CB8, CB9,

Continguts

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

Activitats

Basic concepts of brain function

Basic concepts of brain function
Teoria
4
Problemes
2
Laboratori
0
Aprenentatge dirigit
0
Aprenentatge autònom
12
  • Teoria: Basic concepts of brain function
  • Aprenentatge autònom: Basic concepts of brain function
Objectius: 1
Continguts:

Introduction to Neuroimage Techniques in Neuroscience

Introduction to Neuroimage Techniques in Neuroscience
Teoria
2
Problemes
1
Laboratori
0
Aprenentatge dirigit
0
Aprenentatge autònom
6
  • Teoria: Introduction to Neuroimage Techniques in Neuroscience
  • Aprenentatge autònom: Introduction to Neuroimage Techniques in Neuroscience
Objectius: 2
Continguts:

Brain functions in brain networks and their connectivity

Brain functions in brain networks and their connectivity
Teoria
2
Problemes
1
Laboratori
0
Aprenentatge dirigit
0
Aprenentatge autònom
6
  • Teoria: Brain functions in brain networks and their connectivity
  • Aprenentatge autònom: Brain functions in brain networks and their connectivity
Objectius: 1 3
Continguts:

Basics of Computational Intelligence

Basics of Computational Intelligence
Teoria
6
Problemes
1
Laboratori
0
Aprenentatge dirigit
0
Aprenentatge autònom
14
  • Aprenentatge autònom: Basics of Computational Intelligence
Objectius: 3
Continguts:

Decoding neurocognitive states with neural networks

Decoding neurocognitive states with neural networks
Teoria
2
Problemes
0
Laboratori
0
Aprenentatge dirigit
0
Aprenentatge autònom
9
  • Teoria: Decoding neurocognitive states with neural networks
  • Aprenentatge autònom: Decoding neurocognitive states with neural networks
Objectius: 3 4
Continguts:

Reward processing and reinforcement learning

Reward processing and reinforcement learning
Teoria
2
Problemes
0
Laboratori
0
Aprenentatge dirigit
0
Aprenentatge autònom
6
  • Teoria: Reward processing and reinforcement learning
  • Aprenentatge autònom: Reward processing and reinforcement learning
Objectius: 5
Continguts:

Computational Intelligence of Vision

Computational Intelligence of Vision
Teoria
6
Problemes
1
Laboratori
0
Aprenentatge dirigit
3
Aprenentatge autònom
11
  • Teoria: Computational Intelligence of Vision
  • Aprenentatge dirigit: Computational Intelligence of Vision
  • Aprenentatge autònom: Computational Intelligence of Vision
Objectius: 6
Continguts:

Metodologia docent

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

Mètode d'avaluació

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

Capacitats prèvies

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