BIRCHAM INTERNATIONAL UNIVERSITY

Faculty of Computer Science

MODULE: Computational Intelligence

Program delivered by distance learning higher education up to a maximum of 36 credits. This module may be combined or completed with other online university courses from this faculty.

DESCRIPTION:

This program explores the adaptive mechanisms that enable intelligent behavior in complex and changing environments. The main focus is centered on the computational modelling of biological and natural intelligent systems, encompassing swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems and evolutionary computation. It provides a wide knowledge of computational Intelligence (CI) paradigms and algorithms to resolve real-world, complex problems within the CI development framework.

Courses list (each subject accounts for 3 credits):

1 BIU Earned Credit = 1 USA Semester Credit (15 hours of learning) = 2 ECTS Credit (30 hours of study).

Artificial Intelligence

Computational Human Intelligence

Academic Supervisor: Patrice Boisseau

More information about this supervisor and online university course instructors at BIU Human Network.

This module is applicable to Specialist, Expert, Bachelor's, Master's and Ph.D. (Doctor) Programs. This distance learning degree program is designed at the postgraduate level – Master’s or Doctoral. This module may be easily adapted to complete the Specialist, Expert or Bachelor’s adult degree program requirements. A further option is the enrollment into the online university courses listed in this module.

* University Course (3 credits): Select 1 subject from this module.

* Specialist Diploma (15 credits): First 5 subjects or select 5 subjects from this module.

* Expert Diploma (21 credits): First 7 subjects or select 7 subjects from this module.

* Bachelor's Degree (130 credits): The Admission certificate issued after submission of the application for admission will show the amount of credit transferred and validated from previous education and experience, and the amount of credits required to complete this undergraduate program's major. Additional courses from other modules of this faculty will be assigned in case that the credits displayed on this module are not enough to complete the bachelor's required credits.

* Master's Degree (35 credits): Select from 3 to 9 subjects from this module depending on the amount of credits transferred from previous education and experience. Add 13 credits corresponding to a final project to the selected subjects.

* Ph.D. (Doctor) (45 credits): Select from 3 to 9 subjects from this module depending on the amount of credits transferred from previous education and experience. Add 18 credits corresponding to a final thesis to the selected subjects.

BIU issues an admission certificate after receiving your complete application for admission. This document will show the amount of credits transferred and validated from previous education and experience, and the amount of credits required to complete the degree program's major. BIU can not perform this evaluation without the complete application for admission.

Courses Description (each subject accounts for 3 credits):

Artificial Intelligence

This course explains first-order logic; decision-theoretic planning techniques; learning methods; Bayesian network inference and learning, problem solving, and the curent achievements of Artificial Intelligence. It shows how to develop intelligent systems by assembling solutions to concrete computational problems and understanding human intelligence from a computational perspective.

Instructor: Patrice Boisseau

Computational Human Intelligence

This course studies the natural intelligent systems and how these work with particular emphasis on the human mind processes. Human intelligence is reviewed from a computational point of view. It focuses on issues and tools for building applications with reasoning and learning capability.

Instructor: Patrice Boisseau

Computer & Natural Language

This course examines the syntax and semantics of natural language from a computer programming perspective. It presents a simplified version of Montague grammar because it provides a unified account of syntax and semantics and also it exhibits a tight technical parallel between the syntactic categories of natural language and programming languages.

Instructor: Patrice Boisseau

Computer Interaction & Learning

This course reviews many techniques and algorithms in machine learning, beginning with simple perceptions and ending up with more recent topics such as Markov models, and Bayesian networks. It provides the ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how and why they work.

Instructor: Patrice Boisseau

Natural Intelligent Systems

This course studies how the natural intelligent systems are all biological and how its work cannot always be understood in purely computational terms. A biological system must be understood in terms of its environment, it ecological niche, and its evolutionary history.

Instructor: Elena Lorente Rodríguez

Intelligent Embedded Systems

This course focuses on principles and algorithms for creating intelligent embedded systems that are able to perform high levels of deduction and adaptation. It investigates how creating intelligent embedded systems requires the integration of computational methods from artificial intelligence, software engineering, and operations control.

Instructor: Patrice Boisseau

Computational Intelligence

This course explores the adaptive mechanisms that enable intelligent behavior in complex and changing environments. The main focus is centered on the computational modelling of biological and natural intelligent systems, encompassing swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems and evolutionary computation. It provides a wide knowledge of computational Intelligence (CI) paradigms and algorithms to resolve real-world, complex problems within the CI development framework.

Instructor: Patrice Boisseau

Systems Neuroscience

This course considers a systems level neurobiology. It illustrates neurobiology using invertebrate and vertebrate systems as well as artificial neural networks. It examines the structure, function, and plasticity of neural maps; visual processing in the retina and cerebral cortex; sensorimotor integration; central pattern generators; neuromodulation; synaptic plasticity; and theoretical models of associative memory, information theory, and neural coding.

Instructor: Francisco Chelos Lopez

Artificial Neural Networks

This course covers the fundamentals and applications of artificial neural networks biologically inspired. It examines the implementation of several neural net topologies and related learning algorithms. It considers recent advances in neural networks, high-speed optical networks, switch architectures, and wireless computing.

Instructor: Patrice Boisseau

Theory of Knowledge

This course explores different theoretical perspectives concerning the process of coming to know the world and oneself. It includes the analysis of the concepts of knowledge, the logical structure of propositions and arguments, and the structure of the justification of our beliefs. It provides a framework to understand reality.

Instructor: Elena Lorente Rodríguez

Learning & Memory Development

This course reviews the principles of learning and memory by examining various learning theories, memory research, perception, information processing, and problem-solving. How are memories stored and retrieved? How is sensory input converted to subjective percept? Is the brain a general-purpose learning machine or a toolbox of innate, specialized processors?

Instructor: Elena Lorente Rodríguez

Cognitive Development

This course will focus on the theories and models of learning as applicable to the fields of education, cognitive psychology and artificial intelligence. It reviews different theoretical orientations to learning and memory, metacognition, analogy, language acquisition, reading, writing, mathematics, concept learning, skill acquisition, and self regulated learning.

Instructor: Elena Lorente Rodríguez

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Professionally recognized and validated degrees.

Accredited (Non USA CHEA). International legalization available.

Non formal and independent education.