برنامج ماجستير الذكاء الإصطناعي

Mission

Given the growing needs in industrial, the mission of the Master of Science in Artificial Intelligence program is to graduate students with a solid knowledge in artificial intelligence and preparing them to compete successfully for jobs in high-demand AI. industry.

Educational Objectives

The Master of Science in Artificial I Intelligence program prepared students to:

Understand algorithms and apply the concepts of programming related to AI.

Apply machine learning, deep learning and data science techniques in different fields and in new environments.

Demonstrate knowledge of the use of AI algorithms and processes in different fields of AI like robotics, machine learning, vision, autonomous driving and knowledge representation and reasoning

Analyze and apply AI concepts and applications available in their chosen filed of interest

Evaluate AI-based solutions in a wide range of applications.

Understand the business needs and jobs markets in AI

Proceed their responsibilities in professional and ethical manners.

 

Learning Outcomes

By the time of graduation, the students will be able to:

Demonstrate and analyse current Artificial Intelligence -based obstacles and research issues

Demonstrate scientific and technological topics in an efficient manner and explain underlying Artificial Intelligence techniques and how they can be incorporated in providing solutions and in different environments.

Identify, investigate, and define AI problems, model and evaluate Solutions and their performance.

Develop solutions for different AI-based problem.

Demonstrate efficient communication skills and written proficiencies when working with professionals in a team.

Program Design

The Master of Science in Artificial Intelligence program at the University of Tabuk is a course-based program along with a project to be submitted at the end of the program during two semesters.

The students are required to complete a minimum of 47 credit hours by both courses and a project.

Detailed Program Design

Detailed Program Design

 

With the approval of a supervising professor, qualified students may be admitted to the program. Master of Science in Artificial Intelligence students must complete 12 courses, 2 research projects (Research Project 1 and Research Project 2) and an internship in one of well-known organizations

In particular, students must complete minimum 47 credit hours, including:

At least 36 course credits that include:

33 course credits of core courses.

3 elective course credit.

8 Project credits: Research Project 1 (4 credits) and Research Project 2 (4 credits).

3 course credits for an internship period.

Master of Artificial Intelligence: Study Plan

Full Time –2.5 Year Plan

1St Semester

المقـــــــــــــرر

Total Credits

Credits

Prerequisite

 

المقابل اللغوى

Th.

Pr.

Code/No

Course Title

أساسيات الذكاء الاصطناعي

3

3

-

-

CSC601

Fundamentals of Artificial Intelligence

الرياضيات الحاسوبية

3

3

-

-

CSC602

Computational Mathematics

البرمجة المتقدمة وإطارات برمجة الذكاء الاصطناعي

3

3

-

-

CSC605

Advanced AI Programming and Frameworks 

طرق بحث

3

3

-

-

CSC608

Research Methods

المجموع

12

12

-

 

 

 

 

 

2nd Semester

المقـــــــــــــرر

Total Credits

Credits

Prerequisite

المقابل اللغوى

Th.

Pr.

Code/No

Course Title

التعلم الآلي

3

3

-

CSC602

CSC603

Machine Learning

الرؤية بالحاسب

3

3

-

-

CSC606

Computer Vision

الروبوتية والأنظمة المضمنة

3

3

-

CSC602

CSC609

Robotics and Embedded Systems

الأنظمة المنطقية الضبابية

3

3

-

-

CSC607

Fuzzy Logic Systems

المجموع

12

12

 

 

 

 

 

 

 

Summer Semester

المقـــــــــــــرر

Total Credits

Credits

Prerequisite

 

المقابل اللغوى

Th.

Pr.

Code/No

Course Title

فترة تدريبية

3

-

-

-

CSC699

Internship

المجموع

3

 

 

 

 

 

 

                               

3rd Semester

المقـــــــــــــرر

Total Credits

Credits

Prerequisite

المقابل اللغوى

Th.

Pr.

Code/No

Course Title

الشبكات العصابية والتعلم الذكي

3

3

-

CSC603

CSC604

Neural Networks and Deep Learning

الروبوتية المتقدمة

3

3

-

CSC609

CSC610

Advanced Robotics

مشروع بحثي 1

4

4

-

Completion of 21 credit hours

CSC624

Research Project 1

المجموع

10

10

 

 

 

 

 

 

4th  Semester

المقـــــــــــــرر

Total Credits

Credits

Prerequisite

المقابل اللغوى

Th.

Pr.

Code/No

Course Title

اساسيات علم البيانات

3

3

-

-

CIS601

Fundamentals of Data Science

مقرر اختياري

3

3

-

Completion of 30 credit hours

-

Elective Course

مشروع بحثي 2

4

4

-

CSC624

CSC625

Research Project 2

المجموع

10

10

 

 

 

 

 

 

 

Elective Courses

المقـــــــــــــرر

Total Credits

Credits

Prerequisite

المقابل اللغوى

Th.

Pr.

Code/No

Course Title

معالجة اللغات الطبيعية

3

3

-

Completion of 30 credit hours

CSC612

Natural Language Processing

أنظمة استرجاع المعلومات

3

3

-

CIS610

Information Retrieval

تحليل البيانات الضخمة

3

3

-

CIS611

Big Data Analytics 

تحليل بيانات الرعاية الصحية

3

3

-

CSC615

Data Analytics in Healthcare

القيادة الذاتية للمركبات

3

3

-

CSC616

Self-driving Vehicles

تنقيب البيانات

3

3

-

CSC617

Data Mining

الحوسبة المتوازية

3

3

-

CSC618

Parallel Computing

التعلم المعزز

3

3

-

CSC619

Reinforcement Learning

الفضاء الذكي وانترنت الأشياء

3

3

-

CSC620

Smart Space and IoT

الذكاء الاصطناعي في المجال الطبي

3

3

-

CSC621

AI for Wearable and Healthcare

مواضيع مختارة في الذكاء الاصطناعي

3

3

-

CSC622

Selected topics in AI

 

 

 

 

Courses

Core Courses:

Code

Course Title

Credits

Prerequisite

CSC 601

Fundamentals of Artificial Intelligence

3

None

Description

This course gives a basic introduction to artificial intelligence (AI) and its applications. Students will study the core concepts and topics of AI including its history, solving problems, algorithmic and learning approaches. Through this course, students will learn how to apply AI methods to solve different problems. This course is an exploration of AI domain and its applications in modern lives.

 

Code

Course Title

Credits

Prerequisite

CSC 602

  Computational Mathematics

 

3

None

Description

This course familiarizes students with theories, fundamental conceptions and their basic applications in probability, mathematics statistics, calculus and linear algebra. The course aims at helping students who have a major in computing, science, and other similar fields to develop skills that are useful in solving mathematical problems; for example, computing skills of statistics and probability, calculus and linear algebrathat are neededas core subjects to proceed with the machine learning course.

 

Code

Course Title

Credits

Prerequisite

CSC605

Advanced AI Programming and Frameworks

3

None

Description

This course introduces students to the dominant programming languages for AI/ML and deep learning. It dives deeply into programming tools, libraries, and frameworks for building research projects. This includes reading and loading datasets, preprocessing data, understanding structure using statistical summaries and data visualization, data manipulation, and cleaning techniques. This course also guides students through learning and implementing popular ML and deep learning frameworks such as TensorFlow, Keras, and PyTorch for symbolic math, used to perform differential programming and linear algebra.

 

 

 

 

Code

Course Title

Credits

Prerequisite

CSC608

Research Methods

3

None

Description

The course aims to familiarize students with the fundamental concepts of research and the importance of research and its methodologies, including theory of science and qualitative and quantitative methods. Also, the course aims to give students skills for understanding the structure of a research paper, critical reading of research paper, developing a research proposal for a master's project and writing a research manuscript.

Students will use these theoretical concepts of research to begin to critically review literature relevant to the field of artificial intelligent and its applications.

 

Code

Course Title

Credits

Prerequisite

CSC603

Machine Learning

3

CSC602

Description

This course introduces the fundamental concepts and functioning of machine learning. It covers theoretical foundations as well as essential algorithms for supervised and unsupervised learning. The practical part will focus on the application of machine learning to a range of real-world problems. The topics include  linear and logistic regression, naïve Bayes classifier, k-NN, decision tree, random forest, support vector machine, clustering, dimensionality reduction, and perceptron.

 

Code

Course Title

Credits

Prerequisite

CSC606

Computer Vision

3

None

Description

This course introduces the students to computer vision throughout the continuum from image processing to computer vision, which can be broken up into low, mid and high-level processes, including Image Acquisition, Image Enhancement, Image Restoration, Morphological Processing, Image Segmentation, Representation & Description, and Object Recognition.

 

Code

Course Title

Credits

Prerequisite

CSC609

Robotics and Embedded Systems

3

CSC602

Description

Robotics and Embedded systems course covers the fundamentals of robotics including position, actuators, and robot coordinate system. Manipulator configuration including axis, angles, and frames are also investigated. Moreover, understanding forward and inverse kinematics as well as Denavit-Hartenberg convention. In the Embedded system part of the course, the following topics are covered: CPU architecture, instruction set, program development, and structured assembly processor.

Code

Course Title

Credits

Prerequisite

CSC607

Fuzzy Logic Systems

3

None

Description

The goal of the course is to familiarize students with theoretical background besides the mathematical models of fuzzy logic and sets. When it is compared to the traditional logical systems, the fuzzy logical theory is closer to human thinking in spirit than the traditional logical system; the Fuzzy logic try to imitate human thinking thus reason in a way that is approximate rather than precise. This course has elements that assist students in understanding how to build fuzzy information representation and processing; this includes approximate reasoning and fuzzy inference. The knowledge enables the students to design intelligent systems, as well as controllers. The course will equip students with knowledge on the recent innovations of fuzzy logic systems, whose example include computing with words, the Interval Type-2 fuzzy systems, and the general Type-2 Fuzzy logic systems

 

Code

Course Title

Credits

Prerequisite

CSC604

Neural Networks and Deep Learning

3

CSC603

Description

This course offers a broad introduction to neural networks and deep learning. It also explores the applications and theories relevant to problem solving using deep learning. The course applies deep learning algorithms to real-life problems in diverse domains such as computer vision, natural language processing, sequence modelling, and more. It covers neural networks, backpropagation, optimization (SGD, RMSprop, and Adam), autoencoders, convolutional neural networks, inception, residual networks, RNNs, LSTM, dropout, batch normalization, Xavier initialization, transfer learning, generative adversarial networks, and deep reinforcement learning.

 

Code

Course Title

Credits

Prerequisite

CSC610

Advanced Robotics

3

CSC609

Description

Advanced Robotics course investigates high-level robotic topics such as velocity kinematics, Jacobian derivation, singularities, performance matrices, trajectory planning, and PID control. In addition, topics like velocity sensing, control theory, path control, dynamics, and automation are also covered.  In the practical part of the course, simulation of manipulators and robot programing are investigated. 

 

 

 

 

 

Code

Course Title

Credits

Prerequisite

CIS601

Fundamentals of Data Science

3

None

Description

The Data Science course concentrates on techniques and methods needed to in the Data Science project life-cycle, which includes data collection, data management and data preprocessing, analysis, presentation, as well as operationalization. This class aims at giving students an introduction to all phases of the process of data process and using modern tools and real data; they will gain hands-on experience of the process. The course includes topics such as data formats, cleaning, and loading; data analysis, data governance, data storage in no-relational and relational store; topping up using cluster computing; and data visualization. They will also store and access various data through using suitable data management tools, database, control accessibility of data that is sensitive, and implement conversions of data in different formats. Finally, students will be capable of presenting the results of data science project using reports and visualizations to be used as a foundation of operationalization.

 

Elective Courses:

Code

Course Title

Credits

Prerequisite

CSC612

Natural Language Processing

3

completion of 30 credit hours

Description

This course provides an introduction to the field of computational linguistics, also called natural language processing (NLP) - the creation of computer programs that can understand and generate natural languages (such as English). We will use natural language understanding as a vehicle to introduce the three major subfields of NLP: syntax (which concerns itself with determining the structure of an utterance), semantics (which concerns itself with determining the explicit truth-functional meaning of a single utterance), and pragmatics (which concerns itself with deriving the context-dependent meaning of an utterance when it is used in a specific discourse context). The course will introduce both linguistic (knowledge-based) and statistical approaches to NLP, illustrate the use of NLP techniques and tools in a variety of application areas, and provide insight into many open research problems.

 

Code

Course Title

Credits

Prerequisite

CIS610

Information Retrieval

3

completion of 30 credit hours

Description

This course studies the basic principles and practical algorithms used for information retrieval and text mining. It covers the tasks of indexing, searching, and recalling data, particularly text or other unstructured forms. The contents also include statistical characteristics of text, several important retrieval models, text categorization, recommendation system, clustering, information extraction, etc. The course emphasizes both the above applications and solid modeling techniques (e.g., probabilistic modeling) that can be extended for other applications.

Code

Course Title

Credits

Prerequisite

CIS611

Big Data Analytics 

3

completion of 30 credit hours

Description

This course gives an overview of the Big Data phenomenon, focusing thenon extracting value from the Big Data 

using predictive analytics techniques. It also focuses on big data phenomenon, the main Big Data tools (Hadoop & Spark) , the potential use in a corporate environment, the use of predictive analytics on big data.

 

 

 

Code

Course Title

Credits

Prerequisite

CSC615

Data Analytics in Healthcare

3

completion of 30 credit hours

Description

This course introduces key concepts and methods in bioinformatics. Emphasis will be put on efficient algorithms and techniques used in common applications for the analysis of genetic sequences. Topics covered: comparison and alignment of two or more sequences, indexing and searching of sequence databases, motif discovery, searching with sequence patterns, gene prediction as well as mapping and assembly of data from genome sequencing. Necessary basic knowledge of molecular biology will be communicated throughout.

 

 

Code

Course Title

Credits

Prerequisite

CSC616

Self-driving Vehicles

3

completion of 30 credit hours

Description

Self-driving cars, have rapidly become one of the most transformative technologies to emerge. They depend on Deep Learning algorithms and they create new opportunities in the mobility sector. This course is an introduction to the practice of deep learning through the applied theme of building a self-driving car. It is open to beginners and is designed for those who are new to machine learning, but it can also benefit advanced researchers in the field looking for a practical overview of deep learning methods and their application.

 

 

 

Code

Course Title

Credits

Prerequisite

CSC617

Data Mining

3

completion of 30 credit hours

Description

In this course we explore data mining interdisciplinary field brings together techniques from databases, statistics, machine learning, and information retrieval. We will discuss the main data mining methods currently used, including data warehousing and data cleaning, clustering, classification, association rules mining, query flocks, text indexing and searching algorithms, how search engines rank pages, and recent techniques for web mining. Designing algorithms for these tasks is difficult because the input data sets are very large, and the tasks may be very complex. One of the main focuses in the field is the integration of these algorithms with relational databases and the mining of information from semi-structured data.

 

 

Code

Course Title

Credits

Prerequisite

CSC618

Parallel Computing

3

completion of 30 credit hours

Description

This course discusses several aspects of parallel computing including parallel architectures, parallel algorithms, parallel programming languages and applications. Students will become familiar with different parallel computing approaches, software design, and programming environments. Also, students will learn how to design, analyze, and implement parallel algorithms for several kind of problems.

 

 

Code

Course Title

Credits

Prerequisite

CSC619

Reinforcement Learning

3

completion of 30 credit hours

Description

This course provides students a solid introduction to the field of reinforcement learning. It also offers the fundamentals and practical applications of reinforcement learning and will cover the latest techniques used to create agents that can solve a variety of complex tasks, with applications ranging from gaming to finance to robotics. The topics include: Markov decision process, Policies, Value Functions & Bellman Equations, Learning and Planning with Tabular Methods, Dynamic programming, Monte Carlo, Temporal difference, SARSA, Q-learning, Function approximation, On-policy methods, Off-policy methods, Eligibility traces, Inverse Reinforcement Learning , Policy gradients, and Actor-Critic (A2C, A3C).

 

 

 

Code

Course Title

Credits

Prerequisite

CSC620

Smart Space and IoT

3

completion of 30 credit hours

Description

The course covers what smart space are, exploring the contrasting visions of how they will transform our urban environments and lives, and considers whether smart cities can be sustainable. Then explains the role that latest and emerging smart networking technologies including Cloud Computing, Virtual Networking, big data analytics, 5G Mobile Networks, Mobile App Development, Unmanned Aerial Vehicles (UAVs), and Data and Network Security, which are creating new opportunities for business, education, research and many other aspects of our daily lives. technology can play in transforming cities and considers challenges such as data ownership, privacy and ethics.

 

Code

Course Title

Credits

Prerequisite

CSC621

AI for Wearable and Healthcare

3

completion of 30 credit hours

Description

This course will present the advantages and challenges of telemedicine services. Special focus is placed on how communication, innovative technology, safety and efficiency are addressed through telemedicine. Also, the course covers Wearable technologies which can be innovative solutions for healthcare problems. The big data generated by wearable devices is both a challenge and opportunity for researchers who can apply more artificial intelligence (AI) techniques on these data in the future.

 

Code

Course Title

Credits

Prerequisite

CSC622

Selected topics in AI

3

completion of 30 credit hours

Description

To highlights the up to date issues in Artificial Intelligence field.  The main purpose of this course is to highlight and investigate selected "special topics" in Artificial Intelligence that are not covered in the other offered courses. Such topics might be interrelated to one or more AI disciplines.

 

 

 

 

 

 

 

 

Research Projects:

Code

Course Title

Credits

Prerequisite

CSC624

Research Project 1

4

completion of 21 credit hours

Description

This course provides students with an opportunity to gather the knowledge and skills learned from the program coursework and conduct a research project with industrial applications. Students are expected to conduct a review of research literature and develop a set of hypotheses for a research project in AI. A research project explaining the hypotheses and alternative remedies to the problem must be submitted to the faculty supervisor at the end of the semester. Students are evaluated based on their research project and oral presentation.  

 

Code

Course Title

Credits

Prerequisite

CSC625

Research Project 2

4

CSC624

Description

The research outlined in the CSC624 proposal must be completed during this course. The final report of the research findings and recommendations should be submitted to the advisor and the results presented. The results should have direct practical applications and / or be available for publication in refereed publications. Students are evaluated based on the submitted research and oral presentation.

 

 

Code

Course Title

Credits

Prerequisite

CSC699

Internship

3

completion of 18 credit hours

Description

As part of student’s academic program and the most valuable step to learn and experience knowledge from a full-time employment, an internship course gives a significant opportunity for students to engage with professional people and gain practical training experiment. Students will be able to apply theoretical concepts to practical or laboratory work.