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AIE 605   Special Topics in Artificial Intelligence


Office Hours :  Any time, please send an e-mail to :

Office No:       14-146

Office Location:  College of Engineering, Building 14.

Office Telephone:  ++ 973 17876286 / ++ 973 17876606

Welcome to the Course
Master of Science in Artificial Intelligence Systems

Course code: AIE 605   Special Topics in Artificial Intelligence

My Research Focus:
Pattern Recognition and Processing
Machine Vision

EEG Classifications and Decoding

Computational Intelligence

Control, How can we build artificial intelligence that understands human acts?
Translation between different languages, Natural language inference


Course credits: 4


Course Textbook:

A: Primary Texts:

[1] Rudolf Kruse et al.,  Computational Intelligence: A Methodological Introduction, 2nd Edition,  Springer, New York, 2016.  ISBN 978-1-4471-7294-9
Bookstore or online, e.g., Springer
[2] David Poole and Alan Mackworth "Artificial Intelligence: Foundations of Computational Agents".

Cambridge University Press, (1st edition: 2010, 2nd edition: 2017). (available online. The section references below are to the 2nd edition.)

B: Other Texts:

[3] Stuart Russell and Peter Norvig Artificial Intelligence: A Modern Approach  Pearson Series in Artificial Intelligence, 2020, Fourth Edition,

[4] C: J-S. R. Jang, C-T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice Hall, 1997,

Other resources used (e.g. e-Learning, field visits, periodicals, software, etc.).



A standard undergraduate-level background in Maths, Computational Skills, and Design. 

The Course will be fully supported by a large number of laboratory computational codes, and practical works.  

Hence,  a student will learn the basics of  Matlab and use it as a support for analysis and design of lab oriented experiments.  

Course description (from the UOB Catalog):

AI has several demanding engineering applications. For this course, depending on the request of the requirements of majority of the M.Sc. Candidates, special topics in AI in engineering will be offered. This course will focus on one (or multiple) of the following topics that is used in Artificial Intelligence.



AI has been tremendously introduced in the area of healthcare and the engineering aspects of tools and devices. Given this fact, this special topic within this course will rather focus on how engineers will use AI tools and how can be applied into verities of healthcare engineering aspects. Related topics are; AI-powered predictive care, engineering tools for connected Network Hospitals, connected care, better patient and staff experiences, in addition to Bigdata Mining and Analysis while relying on engineering uses.



AI has also been fully utilized within several sectors related to engineering industry and the Industry 4.0. Given this fact, for the industrial use of artificial intelligence in Industry 4.0 engineering, it is therefore necessary to consider concepts that include distributed, high-performance engineering hardware together with adapted algorithms and coding algorithms. Local analysis of data, enables independent operation and establishes inherent. data security. Processing as close as possible to the data sources process-integrated sensors, low latency and improved quality of results in analysis. This special topic will help to establish standards related to the use of AI in engineering the work for Industry 4.0.



An important special topic of AI is in daily life and related use of AI in education. AI can affect large number of sectors related to education and related engineering of devices used for that purpose. This includes the engineering of systems related to classrooms monitoring, home use of distance education, student support, which is a growing use in higher education institutions. Schools utilize machine learning in student guidance. AI applications can indeed help students automatically schedule their course load. Others recommend courses, majors, and career paths—as is traditionally done by guidance counselors or career services offices. Students performance with similar data profiles performed in the past. Within this special topic of AI in education,we are coloring the educational profile and enhancing its discrepancies according to students profiles.



AI has found itself within the civil engineering and transportation arena. For example, safety of passengers, walking pedestrians, and for drivers has always been the number one concern for the transportation. There are several models that have been validated for the use of AI for civil engineering sectors and applications. With benefits of AI models so far, and rather than decrease the number of human errors; transportation analytics assists in minimizing effects of driving hazards in crowded areas, while also monitoring safety regulation compliance and vehicle maintenance reports, AI can provide better management for the transportation sector. This also includes, Plan and schedule efficiently, Predict and monitor of traffic for engineering works, and safety for road users.



Artificial Intelligence has been extensively applied and used within the robotics industry. Within this specialization, candidates will be more focused towards design of engineering-based systems for robotics applications. This includes drones, vision systems, motion and navigation systems, and building interactions with human behavior. (VI) NATURAL LANGUAGE PROCESSING SYSTEMS: Deep studying of principles behind human language and the related building of systems. This topic also includes the use of Machine Learning tools for building of AI hardware for related applications. Mapping to given input in natural language into useful representations (written and speaking).

Teaching Structure:
The course is consisting of two (2-Hours) in-class sessions per week, making it (4-Hours) a week.
The course content is delivered in a lecture format, with four assignments, a midterm, and a final exam (A Project Work).

Assessment and Grading:
Up to about 3-5% for optional Extra Credit Projects. 

There is roughly one homework assignment per 3-weeks, aside from weeks with exams.

Homework Assignments: 30%
Midterm: 40%

Final Exam, with Presentations: 30%



Object Oriented Programming using Matlab (OOP),

Lectures Notes: Access via UoB Blackboard link:

uses of classes (class), objects (obj) and data structure (struct) .. if you would like to use this approach in programming for this course, this will be great. 

This is optional, but it is always good to learn latest advanced programming tools. 

Download slides about Matlab from the Advanced MATLAB for Scientific Computing, Stanford University. 

Course QAAC Form:  Press to Download 

Object Oriented Programming using Matlab (OOP):

Object Oriented Programming using Matlab (OOP), uses of classes (class), objects (obj) and data structure (struct) .. if you would like to use this approach in programming for this course, this will be great.  This is optional, but it is always good to learn latest and advanced programming tools. 

Download slides about Matlab from the Advanced MATLAB for Scientific Computing, Stanford University.  OOP-Matlab press to download

prereq.  :       

Coding:   AI,  

Genetics, Neural Net,  

NPL, and Pattern Recognition Problems,

Foraging,  Fuzzy-Neural, 

Learning,  Evolution.

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