Artificial Intelligence
MODULE: Artificial Intelligence
Program delivered by distance learning higher education up to a maximum of 48 credits. This module may be combined or completed with other online university courses from this faculty.
DESCRIPTION:
The program of Artificial Intelligence presents a unified and coherent picture of the field. The contents focus on the topics and techniques that are most promising for building and analyzing current and future intelligent systems. It covers the most effective modern techniques for solving real problems. Leading edge AI techniques are integrated into intelligent agent designs, using examples and exercises to lead students from simple, reactive agents to advanced planning agents with natural language and learning capabilities.
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 Perception & Knowledge Computer & Human Interaction Computer Vision & Recognition Computer & Natural Language Computer Interaction & Learning Natural Intelligent Systems Intelligent Embedded Systems Artificial Embodied Intelligence Computational Intelligence Systems Neuroscience Artificial Neural Networks Computer Systems Performance Theory of Knowledge Intelligent Robots
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
Perception & Knowledge
This course examines the complex process by which information is gathered by our sensory organs and converted to a subjective precept. The acquisition and communication of knowledge demands a coherent cognitive framework within which we can reason about events and states that affect us. The course deals with what frameworks are plausible, and how do these choices influence our deductive and creative processes.
Instructor: Elena Lorente Rodríguez
Computer & Human Interaction
This course considers human and computer interaction focusing on questions and tools for building interface applications with mutual reasoning and solving capability. It explains and applies the major mechanisms for control of complexity in large programming and computer systems. It analyzes computational systems to generate computational solutions to abstract problems.
Instructor: Jose A. Cordova
Computer Vision & Recognition
This course explains how computer vision systems can detect, track and recognize people and other objects that will enable new perceptual interfaces between man and machine. It surveys the algorithms, image processing, pattern recognition and techniques involved in computer vision-based perception.
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
Artificial Embodied Intelligence
This course examines what it takes to build intelligent systems that have physical embodiment. After providing a grounding in core artificial life concepts, it looks at the specific problems this presents, historical solutions, and contemporary research into the area of autonomous embodied systems.
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
Computer Systems Performance
This course evaluates computer systems performance considering design, manufacturing and life cycle. It explores testing methods of computer systems, performance verification, inspections, quality assurance, measurement and prediction of computer reliability,maintenance and reuse.
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
Intelligent Robots
This course explores the world of intelligent robots. Intelligent robots can be characterized by the ability to autonomously plan and execute motion sequences to achieve a goal specified by a human user without detailed instructions. This is in contrast to most current industrial robots that have to be pre-programmed with very little adaptability in their task execution. Intelligent robots can also be characterized by the ability to operate in an uncertain, changing environment with the help of appropriate sensing, for example, computer vision.
Instructor: Patrice Boisseau
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Professionally recognized and validated degrees.
Accredited (Non USA CHEA). International legalization available.
