Master in Artificial Intelligence and Natural Language Processing - International Suleiman University

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About the Program

ABOUT THE PROGRAM

The Master of Science in Artificial Intelligence and Natural Language Processing program aims to provide graduate students with foundational and advanced knowledge in topics related to artificial intelligence and natural language processing.

Natural Language Processing

Natural language processing focuses on developing systems that allow computers to communicate with people using spoken languages. Natural language generation systems convert information from computer databases into human-readable or audible language, and vice versa. These systems also allow the completion of complex tasks such as translation, understanding semantics, summarizing texts, and conducting dialogues. The list of applications of natural language processing algorithms includes: interactive voice response, automatic translators, digital personal assistants (such as Siri, Cortana, Alexa); interactive chatbots, and intelligent word processors.

Objectives

Integrate the scientific and technological principles that underpin the field of artificial intelligence and natural language processing.
Apply the research process by critically evaluating AI and NLP solutions to practical problems.
Use professional tools and modern technologies to define, design and develop artificial intelligence and natural language processing solutions.
Communicate technical information both explanatory and written with high efficiency.
Review and critically evaluate recent developments and research topics in the field of artificial intelligence and natural language processing.
Define and apply the legal and ethical principles that govern the field of artificial intelligence and natural language processing.
Provide students with knowledge regarding the current capabilities and limitations of artificial intelligence and natural language processing.
Enhance students’ understanding of the relationship between artificial intelligence, natural language processing, data science and other technological fields.
Demonstrate proficiency in core practices and principles in artificial intelligence and natural language processing.
Provide students with the knowledge and skills to take a leadership role in the advancement of artificial intelligence and natural language processing.
Provide students with an understanding regarding the role of artificial intelligence and natural language processing in different organisations.
Strengthen students’ ability and potential to use critical thinking and problem-solving skills in artificial intelligence and natural language processing.

Learning and Assessment

The First Stage

The student studies eight courses, distributed as follows:
√ Six compulsory courses.
√Two elective courses from among the courses offered by the faculty for master’s students.
The study is conducted through research seminars in each course, and the research seminar relies on multiple references and is in accordance with the scientific research methodology and standards.
The study of each of the eight courses takes four credit hours for a minimum of four weeks, and it may be more than that according to the capabilities of each student, after which the student’s competency and knowledge test is held in the course he finished, then he starts in another course in the same way, and so on.
Courses studied in the first year, the student has the right to extend the study for a period not exceeding a second year.
If a specialization track is chosen within the general program, elective courses will have to be studied from the courses designated for the chosen specialization.

The Second Stage

● The student is assigned a virtual course that the faculty chooses from among the courses that the student studied at the bachelor’s level. This is a practical training for the student, with ten credit hours. The student must divide this course into twelve to fourteen abbreviated lectures. The student presents each lecture in the form of a written summary of its topic in Word format, accompanied by a video recording of it in the student’s voice using the Power Point program. Its duration is not less than ten minutes and not more than twenty. Accurate.

The Thrid Stage

Requirements for registering a thesis topic for a master’s degree in Applied Sciences.

The student must pass the prescribed academic courses with at least 70%.
The student obtains a TOEFL certificate with a score of at least 450, or its equivalent, or obtains a corresponding certificate in the French language, with the exception of those who obtained a first university degree in one of the two languages, or in one of the two languages.
The student submits a request to the university administration to register a master’s thesis with a suggested topic in one of the sub-specialized tracks.
If the initial approval of the subject title is achieved, the Faculty Council shall specify a supervisor to guide the student and follow him up in preparing the plan.
The research plan includes the importance of the subject and a critical presentation of the previous studies in it, and a specification of the research problem, then defining the methodology of the study and its main hypotheses or the questions that you want to answer, and the division of the study and its sources.
The student presents his proposed plan in a scientific seminar. The plan discusses a topic and methodology.
The student amends his plan based on the notes of the professors in the seminar if he is asked to amend it.
The plan is presented after the seminar to the Faculty Council to take its decision regarding the registration of the subject.
In the event of approval, the decision of the Faculty Council is presented to the University Council to approve the registration, and the date of registration is calculated from the date of approval by the University Council.

Jury discussion and degree awarding

The minimum period for preparing a master’s thesis is nine months, starting from the date of approval of the University Council to register the subject, and the maximum is two years, which can be extended for a third exceptional year upon the recommendation of the supervisor and the approval of the Faculty Council, provided that the total period of the student’s enrollment in the degree does not exceed four years.
The supervisor submits a semi-annual report that includes what has been accomplished, and what is required in the remaining period.
After the student completes the thesis and the supervisor reviews it, the supervisor submits to the university administration a report stating that it is valid for discussion, including an evaluation of the student’s performance during the thesis preparation period of 140 degrees, along with submitting a full copy of the thesis signed by him, and a letter with the names of the jury proposed by the professors of the specialty, for presentation to the Council the faculty.
It is required that before the student’s discussion, at least fifteen days have passed from the date of approval of the jury committee from the faculty.
The jury committee formed to discuss the thesis is six months, which may be renewed for a similar period based on a report from the supervisor and the approval of the Faculty Council.
The period of validity of the committee formed to discuss the thesis is six months. It may be renewed for a similar period based on a report from the supervisor and the approval of the Faculty Council.
Each member of the jury writes a detailed scientific report on the validity of the thesis for discussion, and evaluates the thesis out of 100 degrees, and the average of the three degrees is taken.
The student may not be discussed unless he obtains at least 70% of the supervisor’s evaluation of his performance and the jury members’ evaluation of the thesis in the individual reports.
Submit a post-dissertation group report signed by all members of the jury evaluating the thesis discussion out of 100.
The thesis is approved after common discussion by the jury with one of the grades shown in the following table:

Definition	Point	Grade
Excellent	4.00	A+
Excellent	3.75	A
Very Good	3.50	B+
Very Good	3.00	B
Good	2.50	C+
Average	2.00	C
Pass on probation	1.50	D+
Pass on probation	1.00	D
Fail	0.00	F

The following grades are not taken into account for the semester or cumulative GPA.

Thesis or project in progress			:	DP
Incomplete			:	I
In progress			:	IP
Registration has been suspended			:	L
The grade has not been decided	:	NGR
Did not take the final exam.			:	NP
Transferred course.			:	T
Withdraw from the course.			:	W
The course covers two semesters, the degree is given at the end of the spring or summer semester.			:	YR
There are no credit hours.			:	NC
One credit hour taken as a private student. The credit hours and the degree do not count towards the degree			:	ND
Re-submitted course, only the last grade is used in calculating the GPA.			:	R
Credit taken as a special student. Credit hours and grade counted towards a degree.			:	S

Credits
Before the 2016-2017 fall semester 1 credit point is equivalent to 1 semester lecture hour. In the 2016 – 2017 fall semester the University introduced the ECTS – European Credit Transfer System.

Academic Calendar
International Suleiman University calendar is based on the semester system. Each semester has a duration of 15 weeks including the week of the final exam. The summer semester is 10 weeks long, including the final exam.

Program Structure

Program Structure
8 courses = 32 credit hours,

 practical training = 10 credit hours

 Master's thesis = 18 credit hours

Courses

Practical Training

Master's Thesis

Curriculum

Core Courses for General Track

Scientific Research Methodology

Course code: MAS101
Course name: Scientific Research Methodology
Credit hours: 4.00

Scientific research methodology
The course includes an introduction to scientific research (definition of scientific research, its purposes, objectives, importance, and characteristics), characteristics of the researcher, methods of obtaining knowledge. Classifications of scientific research methods, the reasons for differences in the classifications of scientific research methods, the descriptive (historical) approach, the experimental approach, the case study approach – the chapters of scientific research, the steps of scientific research and how to formulate them (title, introduction, problem, hypotheses, questions, objectives, importance, Study limits, terminology, study procedures, data collection tools, questionnaire, observation, interview, samples (types, selection), statistical methods used in research, documentation of sources and references (various methods), recommendations and proposals in scientific research.

Advanced Applied Studies in Programming

Course code: MAIA101
Course name: Advanced Applied Studies in Programming
Credit hours: 4.00

This course aims to introduce students to advanced methods of programming with Python, including object-oriented programming, parallel programming, data structures, algorithms, and applications of Python programming for artificial intelligence and data science. The course covers the following major modules: (1) Object-Oriented Programming, (2) Data Structures and Algorithms, (3) Parallel Programming, (4) Applications and Computing Tools: Features, Libraries, Proxies, Controllers and Regular Expressions applied for NLP tasks.

Advanced Artificial Intelligence

Course code: MAIA102
Course Name: Advanced Artificial Intelligence
Credit hours: 4.00

This course covers basic and advanced concepts and techniques in the field of artificial intelligence. The list of major topics includes intelligent agents, uninformed and informed research, adversarial research, the constraint satisfaction problem, uncertain knowledge and reasoning, covering Bayesian networks, and decision networks. In addition, advanced topics will include machine learning, reinforcement learning, natural language processing (or vision/robotics), and deep learning.

Advanced Applied Language Processing

Course code: MAIA103
Course name: Advanced Applied Language Processing
Credit hours: 4.00

Natural language processing (NLP) focuses on system development that allows computers to communicate with people using everyday language. Natural language generation systems convert information from the computer database into readable or audible human language and vice versa. Such systems also enable sophisticated tasks such as inter-language translation, semantic understanding, text summarization and holding a dialog. The key applications of NLP algorithms include interactive voice response applications, automated translators, digital personal assistants (e.g., Siri, Cortana, Alexa), chatbots, and smart word processors.

Machine Learning

Course code: MAIA104
Course name: Machine Learning
Credit hours: 4.00

This course provides a broad introduction to machine learning. Major topics include regression, classification, and clustering. Detailed topics Simple and Multiple, Ridge feature, kernel, feature selection and Lasso; Linear classifiers and logistic regression. Decision trees and ensemble learning, support vector machines, and artificial neural networks. In addition, machine learning best practices such as overfitting/regulation and bias/variance theory should be covered. Students will learn how to define and implement machine learning algorithms suitable for a variety of problems.

Foundations of Data Science

Course code: MAIA106
Course name: Foundations of Data Science
Credit hours: 4.00

Data science is an interdisciplinary field that provides tools to extract insights from data in various forms, whether structured or unstructured. The Data Science course provides theories, strategies, and tools for understanding and applying the following topics: data preparation, data cleansing and integration, data analysis, classification, clustering, text analysis, and visualization.

Elective Courses for General Track

Development Computing

Course code: MAIA151
Course name: Development Computing
Credit hours: 4.00

This course introduces the main concepts, technologies and applications in the field of evolutionary computing. Topics covered include Components of Evolutionary Algorithms, Genetic Algorithms, Evolution Strategies, Genetic Programming and Learning Classification Systems, Handling of Constraints, Multiobjective Cases, Optimization of Nonstatic Functions and Noise, Coevolutionary Systems, Reactive Evolutionary Algorithms, Evolutionary Computational Theory, Hybridization Using other techniques: Memetic algorithms, ant colony optimization.

Computer Vision and Image Processing

Course code: MAIA152
Course name: Computer Vision and Image Processing
Credit hours: 4.00

An introduction to basic and advanced concepts and techniques in computer vision and image processing. After completing this course, students will be able to apply a variety of computer technologies to design efficient algorithms for real-world applications, such as optical character recognition, face detection and recognition, motion estimation, human tracking, and gesture recognition. Topics covered include image filters, edge detection, feature extraction, object detection, object recognition, tracking and motion analysis, gesture recognition, composing images and camera models, and stereoscopic vision. The course will cover deep learning concepts with an introduction to the various architectures and their applications.

Mining Data

Course code: MAIA153
Course: Mining Data
Credit hours: 4.00

Data mining has become one of the most interesting and rapidly growing fields. Data mining techniques are used to uncover hidden information, such as patterns, in databases and to make predictions. The data to be extracted may be complex data including multimedia, spatial and temporal. Topic includes data processing, association rules, grouping, and classification. This course is designed to provide postgraduate students with a solid understanding of data mining concepts and tools.

Computational Engineering

Course code: MAIA154
Course name: Computational Engineering
Credit hours: 4.00

The list of primary topics includes: finding convex structures, art gallery problems, computing Voronoi diagrams, linear segment intersection, linear programming, point location, stochastic algorithms, and Delauney triangle arithmetic. In addition, we will learn about the following data structures: kd trees, range trees, interval trees, segment trees, and quadtrees.

Directed Studies

Course code: MAIA155
Course Name: Directed Studies
Credit hours: 4.00

This course assists the student in exploring particular areas of interest or enables them to develop in-depth research in their area of interest. The topic should be related to the area of interest for which the student plans to prepare his thesis. The course aims to complete the student’s knowledge while allowing him/her to develop his/her critical thinking and analysis. Enrollment in this course and its subject must be approved in advance by the student’s prospective thesis supervisor and program coordinator.

Computational Robots

Course code: MAIA156
Course Name: Computational Robots
Credit hours: 4.00

Deep Learning Network Applications

Course code: MAIA157
Course Name: Deep Learning Network Applications
Credit hours: 4.00

This course provides coverage of several application areas in artificial intelligence that make use of deep learning (DL) networks. Topics include Introductions to Python, Keras and TensorFlow, Dealing with Big Data, Organization and Leakage, Convolutional Neural Networks (CNN), Time Series Analysis Using Long Term Memory (LSTM), Generative Adversarial Networks (GANs), Transfer Learning, Reinforcement Learning, Applications in computer vision, applications in NLP, and the development of other deep neural networks such as adapters.

Applied Human Interaction with the Computer

Course code: MAIA158
Course Name: Applied Human Interaction with the Computer
Credit hours: 4.00

This course aims to introduce students to the basics of HCI with their application in the design and development of the new user interface using the latest interaction mechanisms. The course covers concepts, methods, and techniques in planning, prototyping, and evaluating user interfaces for interactive systems. Topics include design principles, usability principles, engineering, user-centered problem-solving, device interaction, and GUI design (2D and 3D interfaces). For application, the course introduces development concepts for console-based user interface design for desktop, mobile, and virtual reality. In addition, the course also introduces the design and development of a natural, console-free user interface design and development for the above systems.

Big Data and Data Analysis

Course code: MAIA159
Course Name: Big Data and Data Analysis
Credit hours: 4.00

Big data has become one of the most important technologies that enable organizations to efficiently store, manage and process massive amounts of data to gain business insights. The Big Data course provides the fundamentals, techniques, and tools for understanding and applying the following big data analytics. Topics covered are: big data types, technologies, analytical tools, numerical analysis, text, image and stream, spatial data applications and remote sensing.

Faculty of Applied Sciences

Business, Technology, Internet and network concept. Core values responsibility ethics goals company concept.

Degree: Master's Degree

Program code: MA106AS

Study method: Distance Learning

Credit hour: 144

How long it takes: 
Full time: 3 years
Part time: 6 years
Limit time: 13 years

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