• Overview
  • Program Objectives & Student Outcomes
  • Major Requirements
  • Degree Plans & Catalogues
  • Course Discription
  • Student Training

The vision of the Department of Computer Science (DCS) is to become a leading institution both regionally and internationally in the field of Computer Science. To advance this vision, the DCS offers a Bachelor of Science (BSc) in Artificial Intelligence (AI) program. This program is designed to equip students with in-depth knowledge and specialized skills in AI, an area that is rapidly transforming industries and driving innovation across sectors. Through this program, students will engage with machine learning algorithms, data analysis, and ethical considerations in AI, preparing them to address complex challenges and contribute to advancements in this dynamic field.

To earn a Bachelor of Science (BSc) in Artificial Intelligence (AI), a student must complete a total of 122 credit hours according to the requirements outlined in the table below.

 

The structure of the BSc. in Computer Science Degree Program:

                                                                                   

 

 

 

 

Program Educational Objectives (PEOs)

Within a few years after graduation, graduates of the computer science program at Sultan Qaboos University will be able to use the knowledge and skills acquired from their academic program to attain some of the following accomplishments.

  • PEO1: Become successful computer science professionals and practitioners who can interact and collaborate effectively in various work environments

  • PEO2: Engage in ongoing lifelong learning and be able to pursue graduate studies at respectable universities.

  • PEO3: Contribute productively to the IT needs of the society, be socially responsible, and become mature societal leaders locally and globally. 

 

Student Outcomes (SOs)

The Computer Science program at SQU enables the students to achieve at the time of graduation the following student outcomes:

  • SO1: Analyze a complex computing problem and to apply principles of computing andother relevant disciplines to identify solutions.

  • SO2: Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.

  • SO3: Communicate effectively in a variety of professional contexts.

  • SO4: Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.

  • SO5: Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline. 

  • SO6: Apply computer science theory and software development fundamentals to produce computing-based solutions.

 

 

Admission to the Artificial Intelligence Major

To apply for a major in the College of Science BSc programs, the students need to complete LANC2058 and 3 introductory science courses. The students who want to major in the proposed BSc program in Artificial Intelligence should satisfy the following requirements:

Cohort

Courses Required to Choose Major

(in addition to LAN 2058)

Minimum Departmental Requirements

All cohorts

COMP2101 + MATH2107 and one of the following courses: BIOL2101, CHEM2101, ERSC2101, PHYS2101 or STAT2101

C in COMP2101 and

C in MATH2107

 

 

Dr. Farha Al Kharousi

 (Office#: 209, Tel. Ext: 2228, e-mail: farha@squ.edu.om)

 

The new B.Sc. in Artificial Intelligence student must complete a total of 122 credits over the span of 8 semesters (4 academic years) after the Foundation Program; out of which 54 credits are Major Requirements and 18 credits are Major Electives. The other 50 credits are distributed into University Requirements (6 credits), University Electives (6 credits), College Requirements (3 credits), College Electives (16 credits), and Department Requirements (10 credits) and Department Electives (9 credits). Graduates from the proposed program will be awarded the degree of Bachelor of Science in Artificial Intelligence. 

The degree plan & degree audit can be downloaded by clicking on the following links:

Cohorts 2024 Onwards Degree plan Degree Audit

 

 

Course Descriptions

 

COMP2101     Introduction to Computer Science 

4 Credit Hours         

Prerequisite: (FPCS0101, FPEL0560) or (FPCS0102, FPEL0560) or (FPCS0101, FPEL0600) or  (FPCS0102, FPEL0600) or (FPCS0101, FPEL0601) or (FPCS0102, FPEL0601) or (FPCS0101, FPEL0602) or (FPCS0102, FPEL0602).

This course introduces some fundamental topics in computer science. This includes numbering systems, data representation, problem solving and algorithm design. Furthermore, the course includes the study and practice of basic programing concepts such as data types, variables, arrays, selection, repetition, data files and functions.

 

COMP2202      Fundamentals of Object Oriented Programming

3 Credit Hours  

This course introduces the concepts of object-oriented programming (OOP) and object-oriented-design (OOD). The course addresses the following topics: Abstract Data Types (ADTs), Classes, Objects, Inheritance, Polymorphism, Exceptions, and Memory Allocation. On Completion of this course students should be familiar with OOP principles and be able to implement them using an object oriented programming language.

 

COMP3203      Introduction to Data Structures and Algorithms

3 Credit Hours  

This course introduces the basic data structures, and algorithms for processing data. It emphasizes how to specify, use, and implement Abstract Data Types (ADT). The course also covers algorithm complexity analysis techniques. Topics covered include ADTs (e.g. lists, stacks, queues, trees, hash tables), and basic sorting, and searching algorithms.

 

COMP3205   Introduction to Database Systems

3 Credit Hours

This course introduces fundamental concepts of database systems, namely structural and functional architectures, data modeling, entity-relationship model, relational model, normalization, database query languages (relational algebra, relational calculus, SQL), physical data storage (file structures and organizations, and indexing), and an introduction to the functionality of database management systems such as transaction management.

 

COMP3401    Introduction to Software Engineering

4 Credit Hours

This is an introductory course to the field of software engineering. It presents the basic principles and concepts of software engineering giving a firm foundation for further course work in the field and computers in general. It gives broad coverage of the most important terminology and concepts in software engineering. Upon completing this course, students will be able to do basic modeling and design, particularly using UML. They will also have a basic understanding of requirements, software architecture, and testing. 

 

COMP3503   Introduction to Computer Systems

3 Credit Hours

The course illustrates how programmers can employ their system knowledge to enhance program development. Topics encompass data and machine-level representation, processor architecture, program optimization, memory management, linking, process control, virtual memory, system-level I/O, network programming, and multithreading with synchronization.

 

Comp3601         Bioinformatics Algorithms

3 Credit Hours

This course introduces key bioinformatics concepts and their related computational techniques. A hands-on approach is adopted to discuss the underlying algorithms currently used to analyze biological data. Major topics covered include Gene and Protein Alignments, Sequence Assembly, Gene Prediction, Structure prediction, Molecular Evolution and Gene Expressions.

 

Comp4445          Summer Training

0  Credit Hours

The student is expected to undertake a department approved practical training on an IT-related topic in a government or private institution in Oman. The training will take place during the normal summer teaching period. A training supervisor from the institution should be assigned. The student is expected to submit a report and the supervisor is expected to submit a statement of student performance.

 

Comp4509         Introduction to Computer Security

3 Credit Hours

This course provides an introduction to security and privacy issues in various aspects of computing, including programs, operating systems, networks, databases, and Internet applications. It examines causes of security and privacy breaches, and gives methods to help prevent them.

 

CSAI2600        Programming Fundamentals for Artificial Intelligence

3 Credit Hours

This course offers a comprehensive introduction to the programming concepts essential for ML and AI at large. Students will learn data types and data manipulation, as well as the fundamental algorithms crucial for implementing ML and AI systems. Through hands-on practice, students will develop proficiency in coding for AI applications, preparing them for advanced studies in the field.

 

CSAI3100         Introduction to artificial intelligence

3 Credit Hours

The course presents and discusses concepts and algorithms at the foundation of artificial intelligence. The course covers the theory of knowledge representation, systematic and informed search algorithms, constraint-satisfaction problems, adversarial search, classification, optimization, logic and rule-based systems, and the basics of machine learning and modern artificial intelligence.

 

CSAI3101      Introduction to Machine Learning

3 Credit Hours

This course provides a broad introduction to Machine Learning. It addresses the theoretical background of learning and equips students with the practical knowledge to apply Machine Learning techniques. Concepts and techniques related to supervised learning, unsupervised learning, and reinforcement learning are discussed and practiced using an adequate programming environment.

 

CSAI3105        Information retrieval and text analytics

3 Credit Hours

The course covers search engine theory, design, implementation and evaluation. Topics include statistical text characteristics, information representation, lexical retrieval models, exact- and best-match retrieval and recent neural retrieval. Algorithms and models including pageRank, LSI, LDA, BM25, and vector space models. The software component includes implementing and evaluating search engines.

 

CSAI4101        Deep Learning

3 Credit Hours

This course explores diverse Deep-Learning methodologies for constructing representations from raw data via multi-layered neural networks. Topics encompass foundational neural networks, convolutional and recurrent structures, deep unsupervised learning, reinforcement learning, and their practical applications across a spectrum of Artificial Intelligence domains.

 

CSAI4102         Mobile Robotics Programming

3 Credit Hours

This course provides the basic concepts and algorithms required to develop mobile robots that move in effective, safe, and predictable ways in complex environments. The course covers the basics of mobile robot controls, kinematic theory, navigation, localization, planning, and mapping.

 

CSAI4103            Introduction to Natural Language Processing    

3 Credit Hours

This course is an introduction to the field of Natural Language Processing (NLP). Topics covered include tokenization, language modeling, tagging and parsing including common algorithms for probabilistic modeling. The course discusses NLP applications such as sentiment analysis, and named entity recognition. Students will learn to apply machine learning for NLP tasks using open-source libraries.

 

CSAI4104            Introduction to Computer Vision

3 Credit Hours

This course focuses on designing and implementing programs for image and video understanding tasks. It covers digital image processing basics like acquisition, enhancement, restoration, edge detection, and segmentation. Students learn techniques for interpreting image/video content for inspection, detection, tracking, and recognition, with hands-on practice in a specialized environment.

 

CSAI4105               Digital Entrepreneurship

3 Credit Hours

This course covers the essentials of launching and managing a digital business. Topics include digital business models, the digital entrepreneurship ecosystem, and the stakeholders’ roles. It also addresses advanced technologies, social and sustainable development, and factors driving the success of digital startups. Students will be able to evaluate business plans and analyze startup case studies.

 

CSAI4106                Competitive Programming in Artificial Intelligence

3 Credit Hours

This course explores advanced concepts and methodologies essential for competitive AI environments. Through hands-on experiences, students will deepen their understanding of crucial AI principles, including dynamic programming, graph algorithms, search algorithms, string manipulation algorithms, and computational geometry, empowering them to develop innovative and efficient AI solutions.

 

CSAI4107             Industry Micro-credentials in AI

3 Credit Hours

This course offers a comprehensive exploration of Artificial Intelligence within various industries, providing students with the knowledge and skills required to leverage AI technologies effectively. Tailored towards professionals seeking to enhance their expertise in AI applications specific to the Industry, this course covers fundamental concepts, practical implementations, and industry-specific case studies.

 

CSAI4200                     Ethics in Artificial Intelligence

2 Credit Hours

This course discusses different aspects of AI ethics, including algorithmic ethics, robotic ethics, digital ethics, professional and user experience ethics. It discusses the roles, pertinent ethical codes, and the responsibilities and rights of the involved professionals and users. It explores the societal, legal, economic, and environmental frameworks surrounding ethical dilemmas, drawing insights from real-life case studies.

 

CSAI5100                   Nature Inspired Algorithms

3 Credit Hours

This course covers modern algorithms that are inspired by nature such as evolutionary algorithms to address computationally complex and challenging problems in optimization. Students will be exposed to methods such as genetic algorithms, differential evolution, neural networks, clonal selection, and swarm intelligence based algorithms, and then apply them to solve real-life problems.

 

CSAI5101             Deep Learning Methods for Natural Language Processing

3 Credit Hours

In this course, students will be exposed to methods of Deep Learning for NLP, allowing for the creation of models for NLP tasks such as language translation, understanding, and generation. Topics covered include: LLMs, distributed representations of linguistic entities via embedding, long-span deep sequence modeling of natural language including RNNs, LSTM, GRUs, Self-attention and Transformers.

 

CSAI5102              Advanced Topics in Computer Vision

3 Credit Hours

This course aims at building the foundation concepts for modern computer vision. It discusses major Deep Learning models for Computer Vision tasks such as CNN, RNN, LSTM, and ViT. It also presents the foundations of Generative and Autoregressive models and 3D Deep Learning architectures. Evaluation of the performance of the models is conducted using deep learning programming environments.

 

CSAI5106              Automatic Speech Recognition

3 Credit Hours

This course discusses main formalisms, models and algorithms necessary for developing ASR applications. Topics covered include anatomy of Speech, Signal Representation, Phonetics and Phonology, Signal Processing and Feature Extraction, Transformation, Hidden Markov Modeling, Language Modeling, Neural Networks such as TDNN, LSTM, and RNN and other recent ML techniques used in speech recognition.

 

CSAI5107          New Trends in Artificial Intelligence

 3 Credit Hours

This course explores recent advancements in Artificial Intelligence (AI), with a focus on emerging trends and innovations. Students will critically analyze the latest AI algorithms and models, and evaluate their implementation.

 

CSAI5108             Virtual, Augmented and Mixed Reality

3 Credit Hours

This course introduces concepts and practical applications related to utilization of virtual, augmented and mixed reality technology in various domains. Topics include spatial computing, 3D modeling, interaction design, and user experience principles. Students will learn about real-world case studies and hands-on projects, gaining practical skills in creating immersive experiences.

 

CSAI5109             Computational Question answering   

 3 Credit Hours

The course covers paradigms and algorithmic approaches to question answering (QA) including information retrieval-based QA and knowledge-based QA and presents document and passage retrieval and methods for answer extraction: feature-based, N-gram tiling, and neural answer extraction. The practical  includes adopting QA modules to improve performance on benchmark datasets using established metrics.

 

CSAI5110           Natural Language Processing for Healthcare

3 Credit Hours

This course discusses Natural Language Processing (NLP) applications for healthcare. Students will learn about common medical ontologies and knowledge databases and other important resources for applying NLP to medical text. NLP applications such as patient outcome prediction, medical question answering, medical text summarization, and medical literature understanding will be discussed.

 

CSAI5111            Generative Artificial Intelligence Models

3 Credit Hours

This course discusses techniques for creating AI models capable of generating new data instances, images, text, and other content. Topics include generative adversarial networks (GANs), variational auto-encoders (VAEs), and other advanced generative models. It covers theory, design, implementation, and practical applications including image synthesis, natural language generation, and creative AI.

 

CSAI5112                machine translation

3 Credit Hours

This course discusses the main approaches and algorithms for developing machine translation systems. Topics covered include designing MT models, creating parallel training data, learning parameters, search algorithms used in MT, and evaluation metrics to assess the quality of MT output. The course also briefly covers related tasks such as dialog response generation and sequence transduction.

 

CSAI5113              Medical Image Analysis

3 Credit Hours

This course equips students with theoretical knowledge and practical abilities in medical image processing and analysis. It covers medical image processing and analysis, teaching denoizing, registering, visualizing, segmenting, and understanding 2D, 3D, and 4D images. It introduces classical techniques and Deep Learning architectures, practiced using SimpleITK, MONAI, TensorFlow, and PyTorch.

 

CSAI5114               Pattern Recognition for Biometrics

3 Credit Hours

This course discusses biometric fundamentals, theory, and hands-on experience for designing multi-level security applications. Addressed topics include an introduction to biometrics, identification, and recognition based on face, iris, fingerprint, speaker, and other biometrics. Ethical issues and concerns related to deploying biometric applications, and future trends will also be discussed.

 

CSAI5115              Reinforcement Learning

3 Credit Hours

This course introduces reinforcement learning (RL) which is a strategy of learning through feedback from interaction with the environment. Covered topics include algorithms like Markov Decision Processes, value and policy iteration, Q-learning, Deep Q Networks (DQN), and Policy Gradient methods. RL algorithms will be explored in various contexts including computer vision, language, and gaming.

 

CSAI5200                   Artificial Intelligence for Cyber Security

3 Credit Hours

This course explores the integration of Artificial Intelligence into cybersecurity practices. It utilizes machine learning models including outlier detection, clustering, classification, and regression, to defend data and systems from cybersecurity threats. Topics include financial crime investigation, malware detection, phishing prevention, ransomware mitigation, and network intrusion detection.

 

CSAI5201               Big Data and Cloud Computing

3 Credit Hours

This course introduces the main concepts and techniques for Big Data and Cloud. It discusses Big Data concepts, database systems such as NoSQL, and Big Data Processing on the Hadoop platform. It discusses the main Cloud Computing enabling technologies and architectures, public cloud infrastructure, Distributed programming models such as MapReduce and deploying Big Data applications on cloud.

 

CSAI5202              knowledge representation and reasoning 

3 Credit Hours

This course presents and discusses principles and practices of knowledge representation (KR) including types of logic suitable for KR, and inference and reasoning in these systems. The course presents ontologies, common sense knowledge, symbolic reasoning, semantic web technologies, classical and modern KR schemes, their computational properties, and algorithms for different types of reasoning.

 

CSAI5901              Graduation Project in Artificial Intelligence I

 2 Credit Hours

This graduation project course allows students to apply their knowledge and skills in artificial intelligence to solve real-world problems. It emphasizes teamwork, user interaction, and soft skills. It requires a structured progress report and oral presentations, including an abstract, introduction, literature review, design solution, anticipated outcomes, conclusions, and references.

 

CSAI5902              Graduation Project in Artificial Intelligence  II

3 Credit Hours

This course is a continuation of the Graduation Project I. It provides students opportunities to strengthen skills essential for developing extensive real-world AI applications. These skills include teamwork, user interaction, solution development, and full-scale software construction.   In addition, it involves evaluations of the final product including both implementation and presentation of the entire project.

 

 

 

         

 

 

 

 

     

 

 

 

 

 

 

The students need to undertake a department approved practical training on an IT-related topic in a government or private institution in Oman. The training will take place during the normal summer teaching period. A training supervisor from the institution should be assigned. The student is expected to submit a report and the supervisor is expected to submit a statement of student performance.

 

  1. Summer Training Course (COMP4445)
  • Required degree course by cohort 2008 and above
  • Performed in IT-related department at private/public employers
  • Pass/Fail grade
  • Conducted during summer time (June-September)
  • Duration: minimum of 6 weeks
  • Cannot overlap with other degree courses
  • Done only once

 

  1. Summer Training Benefits
  • Get exposed to real-world
  • Build relation with industry (Job Market)
  • Get exposed to new technologies
  • Others…

 

  1. Entry Requirements
  • Completion of 80 credits (on 122 CH plan)
  • CGPA of 2.0 or above
  • Majoring computer science
  • No other degree course is possible with conjunction of Summer Training

 

  1. Completion Requirements
  • Spending minimum of 6 weeks (full-time; 5-days a week, 5-7 hours a day; expected total training hours:  150)
  • Submission of Training Appraisal Form (TAF)
    • Must be filled, signed & stamped by the training center
    • To be submitted one week after the training ends
  • Submission of Training Feedback Report (TFR)
    • Individual report to be written and submitted by each student
    • To be submitted one week after the training ends

    

        e.  Summer Training Life Cycle

 

Capture

 

             f. Training Places

The following are the training places in the previous years.

Government Sector

Private Sector

Armed Forces Hospital

Ibri Applied Science College

Ministry of Civil Service

Ministry of Education

Ministry of Manpower

Ministry of Tourism

Ministry of Trans. & Telecomm.

Muscat Securities Market

Nizwa Applied Science College

Oman College of Tourism

Oman Olympic Committee

Public Authority for Civil Aviation

Public Authority for Social Insurance

Royal Oman Police.

Royal Opera House.

State and Governor of Dhofar

Sultan Qaboos University

Alamah

Bank Muscat

Cinnamon Software Solution

Ibex

L.N.G.

O.I.F. CO.

O.X.Y

Oman Airports Management Co.

Oman Central Bank

Oman Medical Specialization Board

Oman Tele

ORPIC

P.D.O.

Shell Co.

Shine Mark

The Rock

The Wave - Muscat

University of Nizwa

 

 

 

  • Eidaad Internship Program

Eidaad is an optional training program that is managed by the Ministry of Higher Education, Research and Innovation with the association of the Oman job market. The program aims to narrow the gap between Industry and Academia and to establish an Internship that lasts for one academic year so that students can engage in a longer period of applied learning.

 

This program provides an opportunity for university/college students with a year of valuable experience, which will give the graduate an edge when they enter the real world after their studies. The students who are successful through the selection process will be designated as Interns and they will embark into a one-year internship at the chosen industry and will resume their final year studies to complete the remaining credits at the campus to graduate in their chosen field of study.

 

EIDAAD program will be substituted in the Computer Science program with two major elective courses:  COMP5591 (0 credits) and COMP5592 (6 credits). More details about the Eidaad internship program is provided in the following links: