Bachelor’s Degree in Artificial Intelligence

This practical course transforms the theoretical concepts introduced in Programming (1) into hands-on programming skills through structured laboratory exercises and small programming projects. Students begin by becoming familiar with an Integrated Development Environment (IDE) and learn how to create projects and source files, write and save code, execute programs, and interpret development and error messages. Students practice using variables, constants, data types, operators, and input/output operations and translate mathematical and logical problems into executable programs. Selection statements and loops are then implemented to create programs involving decision making and repetitive processing. Laboratory activities include defining and calling functions, passing parameters, processing return values, and decomposing solutions into organized functional components. Students also practice working with arrays and strings and perform fundamental operations on collections of values and textual information. Particular emphasis is placed on debugging. Students learn to interpret error messages, trace program execution step by step, use breakpoints and available debugger facilities, and distinguish among syntax, runtime, and logical errors. Exercises progress from short programs focusing on individual concepts to problems requiring the integration of several programming constructs. Toward the end of the course, students complete a simple program or mini-project integrating the principal skills developed during the semester. Upon completion, students should be able to use a programming development environment effectively, independently write, execute, and test basic programs, systematically diagnose and correct common errors, and organize source code according to sound introductory programming practices.

Type

Major Requirement

When

Year one, First semester

Prerequisite

None

Unlocks

Programming (2) - Lap

This course develops the language competencies students need to understand and use English in engineering, information technology, and computing contexts. It emphasizes the acquisition of fundamental technical vocabulary related to computers, software, networks, data, and digital systems and trains students to understand and use this terminology accurately in academic and professional situations. The course develops technical reading skills through textbooks, simplified technical articles, user manuals, and hardware and software documentation. Students practice identifying main ideas, understanding supporting details, interpreting terminology from context, and following technical instructions, procedures, and specifications. In writing, students learn to construct clear technical sentences and paragraphs and prepare descriptions, instructions, summaries, and basic professional correspondence. They practice describing devices, systems, and technical processes using accurate terminology and clear organizational structures. The course also develops listening and speaking skills through technology-related materials and discussions. Students learn to explain technical information orally, participate in academic and professional conversations, collaborate in group activities, and deliver short presentations on topics related to their fields of study. Upon completion, students should be able to read and understand technical texts appropriate to their academic level, recognize and correctly use essential terminology in computing and information technology, produce basic technical and professional written texts, and communicate technical information orally with greater accuracy and confidence. The course supports students in working effectively with English-language textbooks, documentation, and other resources used throughout their subsequent specialized courses.

Type

College Requirement

When

Year one, First semester

Prerequisite

None

This general education course introduces nursing students to the foundational knowledge of the Holy Qur'an and its related sciences (Ulum al-Qur'an). The course focuses on the revelation, compilation, preservation, and main themes of the Qur'an, along with fundamental rules of recitation (Tajweed) and thematic interpretation (Tafsir). Special emphasis is given to health-related Qur'anic concepts, ethical values, human preservation, spiritual care, and therapeutic communication in clinical nursing practice.

Type

University Requirement

When

Year one, First semester

Prerequisite

None

This general education course focuses on enhancing fundamental Arabic language skills—reading, writing, speaking, and listening—for nursing students. The course emphasizes correct grammar, sentence structure, functional writing, and professional communication skills necessary for academic success and clear medical documentation in Saudi healthcare settings.

Type

University Requirement

When

Year one, First semester

Prerequisite

None

This course provides students with an integrated foundation in Artificial Intelligence (AI) and Data Science, introducing the fundamental concepts, terminology, problem types, and relationships among data, algorithms, and intelligent systems. The course begins with the concept, evolution, objectives, and major applications of AI and distinguishes conventional computational systems from systems that demonstrate intelligent capabilities. Representative applications from healthcare, education, business, transportation, and intelligent systems are discussed. Students are introduced to the major subfields of AI, including Machine Learning, Computer Vision, Natural Language Processing (NLP), Robotics, and Knowledge-Based Systems. These areas are presented at a conceptual level to clarify their objectives, characteristics, and relationships without introducing advanced algorithms that are addressed in subsequent specialized courses. The course also introduces Data Science, including the concepts of data, information, and knowledge; structured, semi-structured, and unstructured data; data sources; and fundamental data-quality considerations. Students examine the general Data Science Lifecycle, beginning with problem definition and data collection and continuing through data understanding, preparation, analysis, solution development, evaluation, and use of results. The concept of learning from data is introduced through a high-level distinction among Supervised Learning, Unsupervised Learning, and Reinforcement Learning, without detailed mathematical treatment or implementation of specific Machine Learning algorithms. Ethical and societal considerations are incorporated throughout the course, including privacy, bias, fairness, transparency, and responsible use of data and AI technologies. Upon completion, students should be able to explain fundamental AI and Data Science terminology, classify major AI applications, identify common forms and sources of data, describe the general stages of a data-driven project, and recognize the technical and ethical considerations that provide the foundation for subsequent study in AI and Data Science.

Type

Major Requirement

When

Year one, First semester

Prerequisite

None

Unlocks

Artificial Intelligence and Data Science (2)

This course provides students with an integrated foundation in the major concepts and components of Information Technology (IT) and establishes the technological background required for subsequent courses in the program. It begins with the concept and role of IT in organizations and society and introduces fundamental computer hardware components, including the CPU, memory, storage devices, and input/output devices, together with basic factors affecting computer-system performance. The course examines software categories, Operating Systems, and the interaction between hardware and software. Students are also introduced to fundamental principles of digital data representation, file types, data storage, and information processing. An introductory overview of Computer Networks and the Internet explains basic communication concepts, data exchange, and common network services. Fundamental concepts of Databases and Information Systems are presented to illustrate how data can be organized, stored, retrieved, and used to support organizational activities. The course also provides a broad overview of contemporary technologies, including Cloud Computing, Mobile Computing, the Internet of Things (IoT), and AI. These areas are introduced to establish technological awareness without duplicating the detailed treatment provided in later specialized courses. Fundamental Cybersecurity concepts are introduced, including account and data protection, privacy, and responsible and safe use of Internet resources and digital services. Upon completion, students should be able to identify the principal components of IT systems, explain their basic functions, distinguish among major categories of digital technologies, and understand the relationships among hardware, software, data, networks, and digital services.

Type

Major Requirement

When

Year one, First semester

Prerequisite

None

This course introduces students to the fundamental principles of programming and develops their ability to analyze problems, formulate algorithmic solutions, and implement those solutions using a modern programming language. It begins with the concept of an algorithm, the program -development process, problem analysis, identification of inputs and outputs, and representation of solutions using flowcharts and pseudocode. The course covers basic program structure, variables, constants, data types, type conversion, arithmetic, logical and relational operators, and input/output operations. Control structures are introduced, including selection statements and iterative structures, and students learn to combine these constructs to solve progressively more complex problems. Functions are introduced as a mechanism for decomposing programs into smaller and more manageable units. Topics include function definition and invocation, parameter passing, return values, and basic modularity. The course also covers one-dimensional arrays, strings, common operations associated with them, input validation, and introductory error handling. Considerable emphasis is placed on producing correct, structured, readable, and testable programs. Students learn to use meaningful identifiers, appropriate code formatting, and consistent programming practices. They also practice tracing program execution and identifying and correcting syntax, runtime, and logical errors. By the end of the course, students should be able to analyze simple and moderately complex programming problems, design appropriate algorithms, implement working solutions, test program output, and systematically identify and correct common errors. The course provides the programming foundation required for Programming (2), Data Structures, and later specialized programming courses in Artificial Intelligence.

Type

Major Requirement

When

Year one, First semester

Prerequisite

None

Unlocks

Programming (2)

This practical course enables students to apply web-design principles through the construction of actual web pages and small websites using HTML and CSS. Students begin by learning to use a code editor, organize project files, create valid HTML documents, and correctly link pages and resources such as images and style sheets. Laboratory exercises cover headings, paragraphs, lists, tables, hyperlinks, images, multimedia, and forms for user input. Students use CSS to control typography, colors, spacing, borders, backgrounds, and other visual properties and practice applying styles using different selectors. The laboratory provides practical experience in page layout and component organization, including common menus, navigation bars, and other interface structures. Students create responsive interfaces that adapt to different screen sizes and use browser developer tools to simulate different devices and diagnose display problems. Students test pages across different browsers where feasible, verify hyperlinks and file paths, and improve project organization and maintainability. Accessibility principles are incorporated through semantic structure, appropriate alternative text for images, and clearly organized forms. During the final part of the course, students develop a small multi-page website integrating the techniques covered throughout the semester. Upon completion, students should be able to transform an initial interface concept into a functional website, organize project resources effectively, test the interface under different viewport conditions, and ensure that the resulting pages are structured, usable, responsive, and accessible.

Type

Major Requirement

When

Year one, Second semester

Prerequisite

None

This course builds on the foundation established in Artificial Intelligence and Data Science (1) and develops a more systematic understanding of how AI problems are formulated and how raw data is transformed into a form suitable for developing intelligent solutions. The course remains introductory and is deliberately structured to prepare students for later courses in AI Algorithms, Data Analysis, and Machine Learning without duplicating their specialized content. Students are introduced to intelligent agents, their environments, goals, states, and actions, and learn how an AI problem can be conceptually described in terms of states, actions, goals, and constraints. The course provides a high-level distinction among rule-based, search-based, and data-driven approaches so that students understand how the nature of a problem influences the selection of an appropriate solution strategy. From a Data Science perspective, students examine common data characteristics and quality problems, including missing values, duplicate records, inconsistent data, noise, and outliers. Fundamental concepts of data preparation are introduced, including cleaning, integration, transformation, and feature representation, without duplicating the detailed statistical analysis addressed in later courses. The course also introduces the roles of training, validation, and testing data, features and labels, the distinction between training and inference, and the general concept of a model. Evaluation is presented as a fundamental component of intelligent-system development without detailed treatment of algorithm-specific evaluation methods. Responsible AI considerations include data quality, bias, transparency, and interpretability. Upon completion, students should be able to formulate an AI problem conceptually, explain the role of data in intelligent-system development, identify major data-preparation stages, distinguish among major categories of AI solution strategies, and describe the general process through which a data-driven intelligent solution is developed and evaluated.

Type

Major Requirement

When

Year one, Second semester

Prerequisite

Artificial Intelligence and Data Science (1)

This course builds upon Programming (1) and moves students from basic procedural programming toward more structured software development using Object-Oriented Programming (OOP). Following a brief review of fundamental programming concepts, the course introduces classes and objects, attributes and methods, constructors, object creation, and interaction among objects. Students examine encapsulation and access control and learn how these mechanisms protect object state and organize responsibilities within software. Inheritance and code reuse are introduced, followed by polymorphism, abstraction, and interfaces, with emphasis on their contribution to extensible and maintainable software. Structured error handling using exceptions is introduced, together with techniques for detecting and responding to abnormal program conditions. The course covers file handling for reading, writing, and storing information and introduces fundamental collection structures for managing groups of objects more flexibly than conventional arrays. The course emphasizes object-oriented design, decomposition of a problem into cooperating classes with clear responsibilities, reduction of code duplication, and improvement of software reusability. Introductory testing, debugging, and refactoring practices are incorporated into the development process. The course deliberately focuses on general programming principles rather than AI or Data Science libraries, which are addressed in later specialized courses. Upon completion, students should be able to design and implement medium-scale object-oriented applications, define appropriate relationships among classes, apply encapsulation, inheritance, abstraction, and polymorphism, work with exceptions, files, and collections, and produce modular, understandable, reusable, and maintainable software.

Type

Major Requirement

When

Year one, Second semester

Prerequisite

Programming (1)

Unlocks

Mobile Application Development (1)Data Structure

This practical course enables students to apply the object-oriented concepts introduced in Programming (2) through progressively structured programming exercises and projects. Students begin by creating classes containing attributes, methods, and constructors and then practice creating and manipulating objects and integrating multiple classes within a single application. Laboratory activities include access control and encapsulation, appropriate inheritance structures, method overriding, and practical use of polymorphism, interfaces, and abstraction. Emphasis is placed on demonstrating how these mechanisms reduce code duplication and improve software extensibility and maintainability. Students use exceptions to handle expected runtime problems in a structured manner and work with files to store and retrieve application data. Fundamental collection classes are used to manage variable numbers of objects and perform operations such as addition, removal, searching, and traversal. Testing and debugging form an important component of the course. Students use breakpoints, inspect object states during execution, identify logical and runtime errors, and apply introductory refactoring to improve existing solutions and reduce unnecessary dependencies among classes. During the final part of the course, students complete a medium-scale mini-project integrating multiple classes, collections or file processing, and exception handling. The project emphasizes sound software organization rather than implementation of AI algorithms. Upon completion, students should be able to translate an object-oriented design into working software, systematically test and debug their implementation, and organize a multi-class programming project according to sound development practices, providing the programming foundation required for Data Structures and specialized AI programming courses.

Type

Major Requirement

When

Year one, Second semester

Prerequisite

Programming (1) - Lap

Unlocks

Mobile Application Development (1) - LapData Structure - Lap

This course provides students with the mathematical and logical foundations used in Computer Science and Artificial Intelligence and develops their ability to reason abstractly, construct arguments, and solve problems systematically. It begins with mathematical logic, including propositions, logical connectives, truth tables, logical equivalence, and inference, followed by predicates, quantifiers, and precise mathematical statements. The course covers set theory, set operations, Cartesian products, relations and their properties, equivalence and ordering relations, and functions and their principal characteristics. Students are introduced to major mathematical proof techniques, including direct proof, proof by contradiction, contrapositive reasoning, and mathematical induction. Fundamental counting principles are studied, including the rules of sum and product, permutations, combinations, and introductory inclusion-exclusion principles. Recurrence relations are introduced as mechanisms for representing recursively defined processes and computational patterns. The course also introduces fundamental graph theory, including vertices, edges, paths, connectivity, and trees, together with selected applications in computing and structured problem representation. Emphasis is placed on establishing the mathematical reasoning required for later computing and AI courses. Probability and statistical inference are deliberately excluded from detailed treatment because they are addressed in the dedicated Probability and Statistics for Data Science course later in the program. Upon completion, students should be able to use mathematical notation and logical reasoning correctly, construct basic proofs, solve problems involving sets, relations, counting, and graphs, and explain the relevance of discrete mathematical structures to algorithms, data structures, and Artificial Intelligence.

Type

Major Requirement

When

Year one, Second semester

Prerequisite

None

This course introduces nursing students to the fundamentals of scientific research methods and evidence-based practice (EBP) in healthcare. Students will learn how to formulate research questions, conduct literature searches, select appropriate quantitative and qualitative research designs, design data collection tools, follow ethical guidelines, and draft a practical nursing research proposal relevant

Type

University Requirement

When

Year one, Second semester

Prerequisite

None

This course provides students with the fundamental concepts and skills required to design and construct clear, well-structured, standards-based web interfaces. It begins with an introductory overview of the World Wide Web, web pages, browsers, and web servers and then focuses on HTML as the principal language for defining the semantic structure of web pages. Students learn to organize headings, paragraphs, lists, links, tables, images, and other page elements. The course introduces CSS for controlling visual presentation, including typography, colors, spacing, borders, backgrounds, selectors, and the application of styles to interface components. Fundamental techniques for organizing page layouts and structuring web content are also introduced. Responsive Web Design receives significant attention. Students learn to design pages that adapt to different screen dimensions and devices and examine basic principles of visual design, consistency, readability, content organization, and navigation. Web forms are introduced as mechanisms for collecting user input, including common input types and basic principles for organizing data-entry interfaces. The course also introduces usability and web accessibility, emphasizing semantic HTML and the need to design interfaces that can be effectively used by diverse users. The course remains focused on fundamental front-end design and does not extend into server-side programming, databases, or AI application development. By the end of the course, students should be able to analyze the requirements of a simple website, design its structure, create interconnected and semantically organized pages, apply responsive styling, and incorporate fundamental usability and accessibility principles.

Type

Major Requirement

When

Year one, Second semester

Prerequisite

None

This course provides first-year nursing students with foundational academic and general English language skills. The course focuses on improving the four core language domains: reading, writing, listening, and speaking. Emphasis is placed on expanding vocabulary, mastering essential grammar rules, reading basic academic and health-related texts, and writing clear paragraphs required for academic success in nursing education.

Type

University Requirement

When

Year one, Second semester

Prerequisite

None

Unlocks

English Language (2)

This course introduces students to the fundamental principles of mobile application development using an appropriate mobile platform and development environment. It develops the ability to design and implement basic mobile applications that make effective use of common device capabilities. The course begins with mobile application architecture, project structure, application components, and the application lifecycle within a mobile operating system. Students learn to design mobile user interfaces using layouts, input and display controls, lists, navigation mechanisms, and event handling. Application resources, including text, images, and files, are introduced together with fundamental techniques for supporting different screen sizes and display characteristics. The course also introduces basic local storage methods, including preferences, simple persistent data, and files. Students learn how to preserve selected application state across sessions and are introduced to mobile permission management and controlled access to selected device features such as location, cameras, or sensors. Good introductory development practices are emphasized throughout the course. Students learn to organize source code, separate user-interface concerns from application logic where appropriate, and use logging and debugging tools to identify and correct errors. Upon completion, students should be able to analyze the requirements of a basic mobile application, design and implement its user interface, manage navigation and user interaction, use local storage and selected device features, and run, test, and debug applications on emulators or physical devices.

Type

Major Requirement

When

Year two, First semester

Prerequisite

Programming (2)

This course provides students with a systematic understanding of fundamental data structures and their relationship to algorithm and software efficiency. Building on Programming (2), the course begins with a review of arrays and references and then examines how data can be organized in memory and how suitable data structures can be selected for different problems. Topics include linked lists, stacks, queues, and priority queues, together with fundamental operations such as insertion, deletion, searching, and traversal. Trees, binary trees, binary search trees, and common traversal methods are also studied. The course covers hash tables, hash functions, collision-resolution techniques, graphs, and common graph representations. Students also study selected searching and sorting algorithms and compare them in terms of time and space complexity. Big-O notation is introduced to describe algorithmic growth as input size increases. Students are encouraged to evaluate efficiency rather than focusing solely on functional correctness. The course provides an important foundation for the later AI Algorithms course. Accordingly, it focuses on general-purpose data structures and algorithm analysis rather than AI-specific search and heuristic techniques. Upon completion, students should be able to design and implement fundamental data structures, analyze their efficiency, select appropriate structures for specific computational problems, and justify their choices based on performance and memory requirements

Type

Major Requirement

When

Year two, First semester

Prerequisite

Programming (2)

Unlocks

Programming for Artificial IntelligenceArtificial Intelligence Algorithms

This course introduces students to the fundamental concepts of relational database design, management, and use and establishes a strong foundation for later work in Data Science and Artificial Intelligence. It begins with the concepts of data, databases, and Database Management Systems and compares database systems with traditional file-based approaches. Students learn the principal stages of database design and use the Entity-Relationship Model to represent entities, attributes, relationships, cardinalities, keys, and constraints. They then learn how to transform conceptual models into relational schemas composed of related tables. The course covers the relational model, primary and foreign keys, referential integrity, and SQL for creating tables, defining constraints, inserting, updating, deleting, and retrieving data. Query topics include filtering, aggregate functions, grouping, sorting, subqueries, and joins. Functional dependencies and normalization are introduced to reduce redundancy and prevent insertion, update, and deletion anomalies. Fundamental normal forms are studied at an appropriate undergraduate level. The course also provides an introduction to transactions, concurrency, database security, backup, and recovery. Upon completion, students should be able to analyze information requirements, design an appropriate entity-relationship model, transform it into a relational database, write SQL statements to manage and retrieve data, and apply normalization and integrity principles to produce reliable and maintainable database structures.

Type

Major Requirement

When

Year two, First semester

Prerequisite

None

This practical course enables students to apply the concepts introduced in Mobile Application Development (1) through structured exercises and working applications. Students begin by configuring a mobile development environment, creating projects, using emulators or physical devices, and understanding project files and resources. Students design user interfaces using layouts and input and display controls, connect these elements to application code, and handle user-generated events. They implement navigation between screens, transfer data among application components, and manage resources such as strings and images. Laboratory activities include local storage, preferences, files, and preservation of selected application state across sessions. Students also practice permission management and controlled access to device capabilities such as location, cameras, or sensors where supported by the selected environment. Testing and debugging receive significant attention. Students learn to interpret logs, use breakpoints, trace execution, and identify causes of application crashes and unexpected behavior. Applications are also tested across different screen sizes and device configurations to illustrate the effects of device diversity on interface design and application behavior. The course culminates in a relatively integrated mini-project combining multiple interfaces, navigation, local storage, and selected device features. Upon completion, students should be able to independently create, run, test, and debug a basic mobile application.

Type

Major Requirement

When

Year two, First semester

Prerequisite

Programming (2) - Lap

This course establishes the probability and statistical foundations required for subsequent study in Data Analysis, Machine Learning, and Data Mining. It focuses on mathematical and statistical reasoning rather than introducing Machine Learning algorithms prematurely. The course begins with fundamental probability concepts, including sample spaces, events, probability rules, conditional probability, independence, and Bayes’ theorem. Students then study discrete and continuous random variables and major probability distributions, including Bernoulli, Binomial, Poisson, and Normal distributions. Expected value, variance, standard deviation, and covariance are introduced as key measures for describing random variables and relationships among variables. The statistics component covers descriptive statistics, including measures of central tendency and dispersion, percentiles, and quartiles. Sampling concepts, sampling distributions, the Central Limit Theorem, and the general concept of statistical estimation are also presented. Students are introduced to confidence intervals and hypothesis testing, including null and alternative hypotheses, significance levels, p-values, Type I and Type II errors. Correlation and introductory linear regression are presented from a statistical perspective without extending into Machine Learning model training. The course emphasizes interpretation of statistical results and understanding underlying assumptions rather than mechanical use of formulas. Upon completion, students should be able to apply fundamental probability principles, summarize and interpret data statistically, use basic sampling and estimation methods, perform introductory hypothesis tests, and understand the probabilistic and statistical foundations required for later data-analysis and predictive-modeling courses

Type

Major Requirement

When

Year two, First semester

Prerequisite

None

Unlocks

data analysis

This practical course enables students to implement and evaluate the data structures introduced in the theoretical course using an appropriate programming language. Students begin with arrays and references and progress to linked lists and operations such as insertion, deletion, searching, and traversal. Stacks and queues are implemented using alternative representations so that students can compare their practical characteristics. Laboratory work also includes binary trees, binary search trees, traversal algorithms, and tree insertion, deletion, and search operations. Students implement hash tables, hash functions, and collision-handling techniques and represent graphs using adjacency matrices and adjacency lists. General graph-traversal algorithms are also implemented. Searching and sorting algorithms are programmed and compared using data sets of different sizes. Students measure execution behavior and relate practical results to theoretical Big-O analysis. Debugging tools are used to identify implementation errors involving references and dynamically organized structures. The course culminates in a mini-project requiring the use of multiple data structures. Students justify their design choices and evaluate performance. Upon completion, they should be able to implement, test, compare, and select data structures effectively for different computational problems

Type

Major Requirement

When

Year two, First semester

Prerequisite

Programming (2) - Lap

Unlocks

Programming for Artificial Intelligence - Lab

This practical course trains students to design, implement, and manage relational databases using an appropriate Database Management System. Students begin by creating databases and tables and defining data types, primary and foreign keys, and constraints, directly applying concepts from the theoretical Databases course. Students use SQL to perform insert, update, delete, and select operations and then progress to more advanced queries involving filtering, sorting, aggregate functions, grouping, subqueries, and joins. Laboratory activities include designing an entity-relationship diagram for a practical problem, transforming it into a relational schema, and applying normalization principles. Students examine redundancy and dependencies and implement referential-integrity constraints. Introductory exercises involving views, transactions, user privileges, backup, and restoration are included where supported by the selected database platform. The course emphasizes organized database scripts, consistent naming conventions, and systematic testing of data and queries. During the final part of the course, students complete a database project beginning with requirements analysis and conceptual modeling and continuing through table creation, relationship definition, data population, and meaningful queries and reports. Upon completion, students should be able to develop a reliable practical database that can serve as a data source for later Data Analysis and AI applications.

Type

Major Requirement

When

Year two, First semester

Prerequisite

None

This course builds upon English Language (1) by introducing first-year nursing students to specialized medical terminology, clinical communication, and health-related academic writing. The course focuses on medical prefixes, roots, and suffixes, taking patient histories, reading clinical case studies, documenting basic nursing notes, and communicating professionally with patients and multidisciplinary healthcare teams.

Type

University Requirement

When

Year two, First semester

Prerequisite

English Language (1)

This general education course introduces students to the history, geography, culture, and social fabric of Palestine. The course covers the historical development of the Palestinian issue from the late Ottoman period to contemporary times, examining key geopolitical milestones, cultural heritage, international law frameworks, and societal resilience. Special emphasis is placed on the health sector's history, social determinants of health in Palestine, and the ethical role of healthcare professionals in serving local communities.

Type

University Requirement

When

Year two, Second semester

Prerequisite

None

This course introduces the fundamental principles of operating systems and their role in managing computer resources and application execution. It begins with operating-system structure and its relationship to hardware and applications and then examines processes, threads, their states, creation, and management. Students study CPU scheduling and common scheduling algorithms and compare them using appropriate performance criteria. Synchronization, critical sections, shared resources, and inter-process communication are introduced together with deadlock conditions and fundamental approaches for handling deadlocks. The course covers memory management, including paging, virtual memory, and page replacement, together with file systems, secondary storage, and input/output management. Protection, user privileges, and fundamental operating-system security concepts are also addressed. The course remains focused on general operating-system principles and does not extend into Cloud Computing or distributed-system topics that are addressed elsewhere in the curriculum. AI-related applications may be used as examples to illustrate the effects of CPU, memory, and process management without changing the scope of the course. Upon completion, students should be able to explain how operating systems manage processors, memory, files, and devices, analyze fundamental scheduling and synchronization problems, and understand how resource-management decisions influence the performance of computational applications

Type

Major Requirement

When

Year two, Second semester

Prerequisite

None

This practical course trains students to use the programming environments and specialized tools required for later Artificial Intelligence and Data Science courses. The laboratory focuses on hands-on implementation of concepts from Specialized Programming in AI rather than repeating theoretical algorithms. Students begin by configuring a Python or equivalent programming environment, using a package manager, creating virtual environments, and working with an IDE or Jupyter Notebook. They practice writing organized scripts, functions, and modules. Laboratory activities include using NumPy for arrays, matrices, and vectorized operations and using Pandas to create DataFrames, load CSV and JSON files, and perform selection, filtering, merging, and grouping. Students practice handling data types, missing values, and simple transformations primarily as programming exercises. Detailed data-cleaning methodology is reserved for the later Data Analysis course. Basic visualization tasks, file and API access, project organization, and result storage are also included. Students gain introductory experience with Git or another version-control system and practice documenting code and notebooks. The course culminates in a mini-project involving the loading of a data set, execution of a sequence of transformations and exploratory programming tasks, and structured presentation of outputs. Upon completion, students should be able to independently use the fundamental programming tools required for subsequent AI and Data Science coursework.

Type

Major Requirement

When

Year two, Second semester

Prerequisite

Data Structure - Lap

Unlocks

Machine Learning - LabRobotics Programming - LapNatural Language Processing - Practical

This course broadens students’ understanding of the role of Information Technology in organizations and society, with emphasis on practical applications of digital systems and services. It examines Information Systems and the relationship among people, processes, data, and technology and considers how information supports organizational operations and decision making. The course surveys applications of databases, networks, Internet services, cloud services, and mobile technologies in business, education, healthcare, and government. Digital Transformation is introduced as the use of technology to redesign processes, services, and user experiences. Electronic services, e-commerce, remote collaboration, content management, and digital communication are also examined. Data privacy, information security, and responsible practices for collecting and using information are discussed. The course considers broader social and ethical implications of technology, including the digital divide, automation, and the impact of AI and digital technologies on employment and society, without addressing internal AI algorithms. Upon completion, students should be able to explain the organizational role of IT, analyze examples of Digital Transformation, identify benefits and risks associated with different technologies, and understand the relationships among technology, processes, users, and data

Type

University Requirement

When

Year two, Second semester

Prerequisite

None

This course develops the programming skills required for practical work in Artificial Intelligence and Data Science while avoiding premature duplication of Machine Learning and advanced Data Analysis content. It builds on Programming (2) and Data Structures and emphasizes widely used programming tools and libraries. Students strengthen their proficiency in Python, or the selected programming language, with emphasis on data structures, functions, modules, packages, file handling, exceptions, and object-oriented features commonly used in scientific applications. The course introduces NumPy or an equivalent numerical library for working with arrays and matrices, vectorized operations, and fundamental linear-algebra computations. Students use Pandas or a comparable data-processing library to load data, create and manipulate DataFrames, select and filter records, merge data sets, group information, handle data types, and work with file formats such as CSV and JSON. Fundamental visualization libraries are introduced from a programming perspective. Students also learn to access APIs, manage packages and virtual environments, and use Jupyter Notebooks or comparable environments for experimentation. The course emphasizes reusable functions and modules, project organization, and introductory version control. Simple search algorithms or data-processing tasks may be implemented as programming exercises, but detailed Machine Learning algorithms and statistical modeling are deliberately excluded. Upon completion, students should be able to configure an AI programming environment, manipulate arrays and DataFrames, read and transform structured data, and develop well-organized scripts and notebooks that provide the practical programming foundation for later Data Analysis and Machine Learning courses.

Type

Major Requirement

When

Year two, Second semester

Prerequisite

Data Structure

Unlocks

Machine LearningRobotics Programmingnatural language processing

This course provides students with a systematic understanding of classical Artificial Intelligence algorithms used for search, reasoning, planning, and decision making. Building on Data Structures, the course focuses on symbolic and search-based AI. The course begins with problem representation using state spaces, including initial states, actions, transition models, goal tests, and path costs. Students study uninformed search algorithms such as Breadth-First Search, Depth-First Search, Uniform-Cost Search, and Iterative Deepening. The course then introduces heuristic search, including Greedy Best-First Search and A* Search. Students examine heuristic functions and important properties such as admissibility and consistency. Constraint Satisfaction Problems (CSPs) are introduced through variables, domains, constraints, backtracking search, and fundamental constraint-propagation techniques. The course also addresses adversarial decision making through game-playing algorithms such as Minimax and Alpha-Beta Pruning. Fundamental knowledge representation and logical reasoning concepts are introduced to show how facts and rules can support inference. Introductory planning concepts are also included at an appropriate level. Upon completion, students should be able to represent AI problems as state spaces, select and implement suitable search strategies, design and evaluate heuristic functions, solve basic constraint-satisfaction problems, and explain the foundations of adversarial search, reasoning, and planning without conflating these techniques with Machine Learning.

Type

Major Requirement

When

Year two, Second semester

Prerequisite

Data Structure

This course provides students with the methodology and practical skills required to understand, clean, explore, analyze, and interpret data before proceeding to Machine Learning model development in later courses. It builds directly on Probability and Statistics for Data Science and Specialized Programming in AI. The course begins with the data-analysis workflow, including formulation of analytical questions, understanding data sets, variables, and data types. Students then examine data-quality assessment, including missing values, duplicate records, inconsistent values, noise, and outliers, together with appropriate methods for detecting and addressing these problems. Data transformation, encoding, scaling where analytically appropriate, data-source integration, and reshaping are introduced as part of preparing information for analysis. A major component of the course is Exploratory Data Analysis (EDA). Students use descriptive statistics and visualization to examine distributions, patterns, trends, and relationships among variables. Common visualizations include histograms, box plots, scatter plots, and correlation displays. Students analyze quantitative and categorical variables, examine correlations and group differences, and apply selected statistical tests whose mathematical foundations were established in the Probability and Statistics course. Considerable emphasis is placed on interpretation and on connecting analytical results to the original question while avoiding unsupported conclusions. Training of classification, regression, or clustering models is deliberately excluded because these methods are addressed systematically in the later Machine Learning course. Upon completion, students should be able to implement a complete data-analysis pipeline beginning with raw data and ending with well-supported findings, visualizations, and documented interpretations.

Type

Major Requirement

When

Year two, Second semester

Prerequisite

Probability and Statistics for Data Science

Unlocks

Data Mining

This general education course introduces nursing students to the fundamentals of human rights, patient rights, and professional healthcare ethics. The course examines international human rights instruments, Saudi national legislation, patient rights charters, bioethics, informed consent, and non-discrimination. Students will explore the relationship between human rights principles and nursing care, focusing on protecting patient dignity, confidentiality, and vulnerable populations in clinical practice.

Type

University Requirement

When

Year three, First semester

Prerequisite

None

This course introduces nursing students to principles of occupational health and safety within hospital and clinical environments. The course covers workplace hazard identification, infection prevention, ergonomics, chemical and radiation safety, needle-stick injury prevention, emergency preparedness, and the psychological well-being of healthcare workers, aligned with Saudi Ministry of Health regulations and Saudi Central Board for Accreditation of Healthcare Institutions (CBAHI) safety standards.

Type

University Requirement

When

Year three, First semester

Prerequisite

None

This course introduces the fundamental concepts, architectures, and technologies underlying the Internet of Things (IoT) and examines how physical devices, sensors, software, networks, and digital services can be integrated to create intelligent connected systems. The course begins with the definition and characteristics of IoT, its principal components, and the distinction between conventional devices and network-connected smart objects. Representative applications are discussed in areas such as smart homes, smart cities, healthcare, agriculture, Industrial IoT, and environmental monitoring. Students study the components of an IoT device, including sensors, actuators, microcontrollers, and embedded platforms, and learn how environmental measurements are captured and converted into digital data. Fundamental device-communication concepts are introduced through suitable wired and wireless technologies. Common application protocols such as MQTT, CoAP, and HTTP are presented together with device-to-device and device-to-cloud communication models. The course examines layered IoT architectures, including device, network, processing, and application layers. An introduction to edge computing explains how data can be processed closer to its source in order to reduce latency, network traffic, and dependence on remote cloud services. Students are also introduced to device identity, data collection, remote monitoring, basic device control, and interoperability among heterogeneous devices and platforms. Resource constraints involving power, memory, and connectivity are discussed together with fundamental security and privacy risks associated with connected devices. Upon completion, students should be able to analyze the requirements of an IoT system, identify its principal components, select suitable communication mechanisms, and design a preliminary connected-system architecture that considers functionality, resource constraints, security, and scalability.

Type

Major Requirement

When

Year three, First semester

Prerequisite

None

This course introduces the engineering principles, processes, and practices used to develop software systems in a systematic, manageable, and maintainable manner. It begins with the concept of Software Engineering and the need for disciplined development approaches when building medium- and large-scale systems. The Software Development Life Cycle (SDLC) is examined from requirements analysis through design, implementation, testing, deployment, maintenance, and evolution. Students study Requirements Engineering, including elicitation, analysis, documentation, and management of functional and non-functional requirements. System modeling is introduced through appropriate representations such as use cases and selected UML diagrams. The course covers fundamental software design principles, including modularity, component decomposition, separation of concerns, cohesion, coupling, and introductory software architecture. Coding standards, documentation, configuration management, and version-control practices are also addressed. Software testing is introduced conceptually through unit, integration, system, and acceptance testing. Maintenance, software evolution, refactoring considerations, and technical debt are discussed to illustrate the long-term nature of software development. The course also introduces Agile Development, including appropriate elements of Scrum, team roles, iteration planning, effort estimation, risk management, collaboration, and software-project management. For Artificial Intelligence students, the course highlights selected differences between conventional software projects and data-driven or AI-oriented software, particularly the role of data, experimentation, and reproducibility, without extending into specialized MLOps practices. Upon completion, students should be able to analyze software requirements, produce suitable models and designs, organize software into manageable components, collaborate effectively within a development team, and apply engineering practices that improve software quality, testability, maintainability, and long-term evolution.

Type

Major Requirement

When

Year three, First semester

Prerequisite

None

This course provides students with the theoretical and methodological foundations required to build and evaluate Machine Learning models. It builds on prior study in Probability and Statistics, Data Analysis, and Specialized Programming in AI and focuses on how patterns and relationships can be learned from data for prediction, classification, and grouping. Deep neural architectures are intentionally excluded because they are addressed in the subsequent Deep Learning course. The course begins with the concept of Machine Learning and the distinction among Supervised Learning, Unsupervised Learning, and an introductory conceptual view of Reinforcement Learning. Students examine features and labels, data preparation for modeling, and the use of training, validation, and test sets. Generalization, underfitting, and overfitting are introduced as central concepts in model development. Supervised-learning methods include Linear Regression, Logistic Regression, k-Nearest Neighbors, Decision Trees, Naïve Bayes, and Support Vector Machines at an appropriate undergraduate level. Emphasis is placed on the underlying concept, training mechanism, assumptions, advantages, and limitations rather than purely on library usage. Unsupervised learning is introduced through methods such as k-Means Clustering, together with introductory dimensionality reduction such as PCA. The course is deliberately distinguished from the later Data Mining course: here the focus is on learning algorithms and predictive modeling, whereas Data Mining emphasizes knowledge discovery and pattern extraction. Students learn model-evaluation techniques using metrics appropriate to different tasks, including accuracy, precision, recall, F1-score, confusion matrices, ROC-AUC, MAE, MSE, and R². Cross-validation and hyperparameter selection are also introduced. Upon completion, students should be able to select appropriate Machine Learning methods, train and evaluate models, diagnose overfitting, compare alternative models, and interpret results and limitations in a scientifically sound manner.

Type

Major Requirement

When

Year three, First semester

Prerequisite

Programming for Artificial Intelligence

Unlocks

Deep Learning

This course provides students with a structured understanding of computer-network principles and how data is transmitted among devices and systems across local networks, wide-area networks, and the Internet. It begins with network components, network classifications, and layered communication models, including the OSI and TCP/IP models and the responsibilities of their major layers. Physical- and data-link-layer concepts are introduced at an appropriate level, including transmission media, frames, MAC addressing, Ethernet, and switching. The course then examines the network layer through IPv4 and IPv6 addressing, subnetting, routing, routers, and path-selection concepts. Transport-layer functionality is studied through TCP and UDP, ports, reliability, flow control, and the distinction between connection-oriented and connectionless communication. Application-layer services include conceptual treatment of DNS, HTTP/HTTPS, and electronic-mail protocols. The course introduces wireless networking and Wi-Fi, basic mobility concepts, and important network-performance measures such as latency, throughput, packet loss, and bandwidth. Fundamental network-security concepts are also introduced, including common threats, firewalls, secure communication, and encryption in transit, while detailed security principles are reserved for the Information Security course. For Artificial Intelligence students, the course establishes the networking foundation required to understand cloud services, IoT systems, distributed data access, and networked AI applications without extending into specialized distributed Machine Learning. Upon completion, students should be able to explain end-to-end data communication, perform basic IP addressing and subnetting, describe routing and transport-layer behavior, diagnose fundamental connectivity problems, and understand how network characteristics influence the performance and reliability of digital systems and applications.

Type

Major Requirement

When

Year three, First semester

Prerequisite

None

This practical course enables students to implement a complete Machine Learning workflow using appropriate programming tools and libraries and to apply algorithms from the theoretical course to realistic data sets. Students begin by preparing data for modeling, identifying features and targets, and correctly partitioning data into training, validation, and test sets while avoiding data leakage. Students establish baseline models and implement supervised-learning methods such as Linear Regression, Logistic Regression, k-Nearest Neighbors, Decision Trees, Naïve Bayes, and Support Vector Machines using libraries such as scikit-learn or equivalent tools. They compare results and investigate how different parameter settings influence model behavior. Laboratory work emphasizes selecting evaluation metrics appropriate to the problem rather than relying automatically on accuracy. Students implement confusion matrices, precision, recall, F1-score, ROC-AUC, and relevant regression metrics. Cross-validation and systematic hyperparameter tuning are also practiced. For unsupervised learning, students implement k-Means and selected introductory dimensionality-reduction techniques and learn to interpret results in the absence of predefined labels. A major objective is the development of reproducible experiments. Students document data splits, random seeds where appropriate, model configurations, and experimental results. They also gain introductory experience saving trained models, loading them, and performing inference on new data. The course culminates in a mini-project in which students select a data set, train several candidate models, evaluate them systematically, and justify the final model choice using quantitative evidence and reasoned analysis.

Type

Major Requirement

When

Year three, First semester

Prerequisite

Programming for Artificial Intelligence - Lab

Unlocks

Deep Learning - Lab

This requirement provides flexibility within the curriculum and enables students to pursue deeper study in a specialized or emerging area consistent with the objectives of the Artificial Intelligence program.

Type

Elective Requirement

When

Year three, Second semester

Prerequisite

None

Information Security protects confidentiality, integrity, and availability by identifying assets, threats, and vulnerabilities before selecting controls. Authentication, authorization, password and data protection, untrusted input, updates, backup, and initial incident response are covered. Students produce a threat model and justified controls for a mobile service. Testing remains within authorized environments. All security testing is limited to authorized environments; specialist penetration testing lies outside the scope. Students mark trust boundaries on a mobile-service diagram, asking where attacker-controlled input enters and what operations it could reach. A strong password and an additional authentication factor are compared under device loss and database leakage; neither is described as perfect. A lost-phone scenario covers session revocation, locally stored information, and backups. Logs are reviewed to separate incident evidence from details that might expose users' secrets.

Type

Major Requirement

When

Year three, Second semester

Prerequisite

None

This course introduces the concepts and methods used in Data Mining to discover useful patterns and knowledge from data sets, building on prior study in Data Analysis and Machine Learning. The course is deliberately distinguished from Data Analysis, which emphasizes understanding and describing data, and from Machine Learning, which emphasizes learning predictive or descriptive models. The central objective here is knowledge discovery and extraction of useful patterns and relationships. The course introduces the Knowledge Discovery in Databases (KDD) process, including data selection, preparation, mining, pattern evaluation, and interpretation. Data preparation is reviewed only as necessary because it has already been covered in earlier courses. A major topic is association-rule mining, including support, confidence, and lift, together with algorithms such as Apriori or equivalent methods and applications such as market-basket analysis. Clustering is revisited in the context of pattern and structure discovery without unnecessarily repeating the full algorithmic treatment from Machine Learning. The course also examines anomaly and outlier detection, pattern discovery, and introductory sequential or temporal pattern mining where appropriate. High-dimensional data and feature-selection considerations are introduced in the context of mining useful structures from complex data. Students learn to evaluate the interestingness and practical usefulness of discovered patterns and distinguish meaningful relationships from accidental findings. Risks such as data snooping, spurious correlations, and overinterpretation are discussed. Applications are drawn from business, healthcare, security, web data, and other domains. Upon completion, students should be able to identify suitable Data Mining tasks, apply appropriate pattern-discovery methods, evaluate and interpret discovered knowledge, and communicate findings while recognizing limitations associated with data quality and inference.

Type

Major Requirement

When

Year three, Second semester

Prerequisite

data analysis

This course develops students’ ability to prepare clear, structured, professional, and academically sound technical documents and reports, with particular relevance to Engineering, Computing, and Artificial Intelligence projects. It begins by distinguishing general writing from technical writing and emphasizes precision, clarity, conciseness, organization, and objectivity. Students learn the structure of a technical report, including titles, abstracts or executive summaries, introductions, background sections, methodologies, results, discussions, conclusions, and recommendations. Appropriate organization of sections and subsections and logical flow among them are emphasized. The course covers effective presentation of technical information through tables, figures, charts, and diagrams, including appropriate captions and in-text references. Students learn to adapt the level of technical detail to the intended audience and to use terminology consistently. Academic referencing and citation management are addressed together with plagiarism avoidance and the distinction among paraphrasing, quotation, and summarization. Ethical reporting practices are emphasized, including accurate presentation of evidence, avoidance of exaggerated conclusions, and transparent reporting of limitations or negative results. For AI-oriented work, students learn to document data sets, methodologies, experimental setups, evaluation metrics, results, and limitations in ways that support reproducibility. The course also includes preparation of technical presentations, project documentation, and selected professional technical communications. Upon completion, students should be able to prepare a complete and logically organized technical report, present data and experimental findings accurately, use references appropriately, document methodologies and experiments clearly, and communicate technical work in forms suitable for academic projects, graduation work, and professional practice.

Type

College Requirement

When

Year three, Second semester

Prerequisite

None

This course introduces nursing students to the fundamentals of Islamic culture, worldview, and moral philosophy, with a specific focus on medical jurisprudence (Fiqh), bioethics, and patient care. Students will study the core tenets of Islamic faith, ethical principles governing human interaction, spiritual care, and contemporary bioethical issues (such as organ transplantation, end-of-life care, and reproductive health) aligned with Islamic legal rulings and Saudi healthcare practices.

Type

University Requirement

When

Year three, Second semester

Prerequisite

None

Cloud Computing covers service models and the division of provider and customer responsibility, with virtualization, containers, scaling, and load balancing. Students choose storage and processing for a mobile-facing API and consider latency, availability, and cost. Permissions, logs, metrics, backup, and recovery support a simple architecture whose component failures can be analyzed. Students compare two designs for performance, availability, cost, and administrative effort, and locate responsibility for protecting data and settings. A hypothetical failure of a component or region asks what users experience, what can be restored, and how much work might be lost. Students estimate monthly cost for a hypothetical service using assumptions about users, requests, storage, and data transfer, then change one assumption to test sensitivity. Increasing one server's size is compared with adding several smaller instances while checking whether the database or network becomes the bottleneck.

Type

Major Requirement

When

Year three, Second semester

Prerequisite

None

This course on self-marketing and personal development addresses self-improvement and life-building, focusing on enhancing human capabilities for the better by acquiring new skills or discovering existing abilities and enabling individuals to benefit from them in various life activities. The course helps students eliminate negative behaviors and adopt positive ones, set clear goals, and develop self-control. It also teaches decision-making skills necessary to build a strong personality capable of shaping a better future and contributing effectively to society.

Type

University Requirement

When

Year three, Second semester

Prerequisite

None

This course systematically prepares students for the subsequent Graduation Project by requiring them to identify a meaningful real-world or research problem in Artificial Intelligence or Computing and transform it into a clearly defined and feasible project. Students begin by selecting a problem domain, identifying relevant stakeholders or users, analyzing the problem, and defining project objectives and research or development questions where appropriate. Students conduct a structured literature or technology review to understand existing approaches, available tools, data, and limitations and to establish the rationale for the proposed project. Software-oriented projects define functional and non-functional requirements, while research-oriented projects identify experimental and methodological requirements. The course includes feasibility analysis and selection of suitable technologies, data sets, algorithms, platforms, and tools. Students prepare an initial system architecture or research methodology. AI projects are expected to identify data sources, data-preparation strategies, appropriate baselines, evaluation metrics, comparison procedures, privacy concerns, and relevant ethical considerations. Project planning is addressed through task decomposition, schedules, deliverables, resource requirements, risks, and mitigation strategies. Students are also expected to define a testing or evaluation plan before implementation begins. The principal output is a comprehensive project proposal including background, problem statement, objectives, related work, methodology, requirements, proposed design, evaluation plan, timeline, and references. Upon completion, students should have a technically feasible and academically defensible proposal that can be presented to an academic committee and used as the direct foundation for implementation in the Graduation Project course.

Type

College Requirement

When

Year four, First semester

Prerequisite

None

Unlocks

Graduation Project

This practical course enables students to build, train, and evaluate Deep Learning models using an appropriate framework such as TensorFlow/Keras, PyTorch, or an equivalent environment. It applies concepts from the theoretical Deep Learning course to realistic data sets and computational experiments. Students begin by configuring the development environment and working with tensors, data sets, data loaders, and batches. They construct feedforward neural networks, define layers, activation functions, loss functions, and optimizers, and implement or use appropriate training workflows. Students monitor training and validation loss, inspect learning curves, and experiment with hyperparameters such as learning rate, batch size, and number of epochs. Techniques including dropout, batch normalization, and early stopping are implemented and compared in terms of their effects on overfitting and generalization. A major practical component involves building Convolutional Neural Networks for image-related tasks. Students prepare image data, apply data augmentation, construct convolution and pooling layers, and evaluate trained networks. Introductory implementation of sequence models such as RNNs and LSTMs may also be included where appropriate. Students gain experience with pretrained models, transfer learning, and fine-tuning, including freezing and unfreezing selected layers to adapt existing networks to new data sets. Reproducibility is emphasized through documentation of experimental settings, hyperparameters, random seeds where applicable, model checkpoints, and evaluation results. Students also gain basic experience using GPU resources when available. The course culminates in a mini-project requiring students to select a suitable architecture, train and evaluate it systematically, analyze errors, and document experimental findings without duplicating the specialized NLP laboratory course.

Type

Major Requirement

When

Year four, First semester

Prerequisite

Machine Learning - Lab

This course develops students’ ability to identify ethical issues and professional responsibilities encountered in Engineering, Computing, and Information Technology and to make professionally responsible decisions based on recognized principles. It begins with the concept of professional ethics, distinctions between legal compliance and ethical responsibility, and the role of professional codes of conduct. Students examine integrity, accountability, truthful reporting, conflicts of interest, intellectual property, and responsibilities toward employers, clients, users, and society. Privacy, confidentiality, data protection, and cybersecurity are considered professional obligations rather than purely technical concerns. Particular attention is given to AI ethics, including bias, fairness, transparency, explainability, accountability, use of personal information, and potential harms associated with automated decision making. The focus is on ethical analysis and governance rather than repetition of AI algorithms. The course also considers professional responsibility when developing software or AI systems that may affect health, safety, rights, opportunities, or access to services. Testing, documentation, risk assessment, disclosure of limitations, and responsible deployment are examined in this context. Case studies are used to analyze conflicts involving performance versus privacy, profit versus safety, and innovation versus social responsibility. Students identify stakeholders, assess possible consequences, and compare alternative actions. Upon completion, students should be able to recognize ethical issues in technology projects, evaluate the impacts of technical decisions on different stakeholders, apply professional principles to decision making, and explain individual and organizational responsibilities associated with the design, development, deployment, and use of AI and digital technologies.

Type

College Requirement

When

Year four, First semester

Prerequisite

None

This course introduces the concepts and programming techniques required to develop robotic systems capable of sensing their environments, making decisions, and executing physical actions. Building on previous specialized AI programming skills, the course focuses on the integration of software, sensors, actuators, and control rather than mechanical robot design. Students begin by examining the components of robotic systems, major robot categories, and representative applications. They learn how software communicates with sensors, actuators, motors, and controllers and are introduced to robot coordinate systems, position, orientation, and movement. Fundamental robot kinematics concepts are presented at an appropriate level to help students understand how motion commands relate to robot movement without extending into advanced mechanical engineering analysis. The course examines sensing and perception, including the acquisition and processing of information from distance, proximity, camera, inertial, or other sensors. Introductory feedback and closed-loop control concepts explain how robot actions can be adjusted in response to sensor measurements. Students are introduced to localization, mapping, path planning, and obstacle avoidance, providing the foundations needed to understand autonomous navigation. An appropriate robotics middleware such as ROS may be introduced through concepts such as nodes, topics, messages, and services. Artificial Intelligence techniques are incorporated where appropriate for search, perception, or intelligent decision making, but previously studied Machine Learning and Deep Learning algorithms are not repeated. Upon completion, students should be able to analyze the architecture of a robotic system, program interactions among sensors and actuators, implement basic movement and navigation behaviors, and design a simplified software architecture for an intelligent robotic application.

Type

Major Requirement

When

Year four, First semester

Prerequisite

Programming for Artificial Intelligence

This practical course enables students to apply robot-programming principles using a physical robotic platform or an appropriate simulation environment. Students begin by configuring the development environment, connecting to a robot or simulator, and running basic programs that control movement and read sensor data. Students send commands to motors and actuators and control parameters such as speed and direction. They acquire information from proximity, distance, inertial, camera, or other available sensors and connect sensor input to robot behavior in order to implement simple reactive systems. Laboratory work includes introductory feedback-based control and experiments demonstrating how control parameters influence accuracy and stability. Students implement obstacle detection and avoidance, line following, or other navigation tasks appropriate to the available platform. Where ROS or comparable middleware is used, students create nodes and work with topics, messages, and services, organizing robotic software into modular components. Visualization, logging, and debugging tools are used to inspect sensor streams and robot state. Selected exercises may involve mapping, localization, and path planning in a simulation environment. Students compare planned behavior with actual or simulated robot movement and identify factors affecting execution. A major emphasis is placed on system integration and testing because robotic applications combine sensing, software, communication, and physical or simulated hardware. Students learn to isolate and diagnose faults occurring at different levels. The course culminates in a small robotic project in which the system performs a defined task with a degree of autonomy. Students test performance and document the software architecture, experimental results, limitations, and technical problems encountered.

Type

Major Requirement

When

Year four, First semester

Prerequisite

Programming for Artificial Intelligence - Lab

This course introduces nursing students to the fundamental concepts of environmental health and its impact on human health and disease prevention. Students will explore air and water quality, climate change, medical waste management, chemical hazards, vector-borne diseases, and environmental safety standards within healthcare system and community settings.

Type

University Requirement

When

Year four, First semester

Prerequisite

None

This course introduces concepts and methods for transforming problems and ideas into innovative and potentially viable ventures, with appropriate emphasis on technology-based products and services. It begins with entrepreneurship and innovation and distinguishes among ideas, inventions, and innovations. Students learn how unmet needs and practical problems can become opportunities for new products or services. The course covers opportunity identification, customer discovery, analysis of users, markets, and competitors, and development of a clear value proposition describing the benefit provided to a target customer or user group. Students are introduced to business models and their major components, including customer segments, channels, revenue streams, costs, key resources, activities, and partners. A tool such as the Business Model Canvas may be used to structure venture concepts. Students develop a Minimum Viable Product (MVP) or prototype, test assumptions with potential users or stakeholders, collect feedback, and iterate before committing substantial resources to full-scale development. The course also introduces pricing, marketing, funding, basic financial feasibility, intellectual property, and risk assessment. For AI-oriented ventures, students consider additional issues associated with data availability, infrastructure, responsible AI use, and the need to demonstrate that AI contributes meaningful value rather than being included solely for technological novelty. Upon completion, students should be able to analyze a problem or opportunity, formulate an innovative solution, develop an initial value proposition and business model, create a prototype or MVP, evaluate basic feasibility, and present the proposed venture effectively to stakeholders or potential supporters.

Type

College Requirement

When

Year four, First semester

Prerequisite

None

This requirement provides students with an opportunity to pursue deeper study in a specialized or emerging Artificial Intelligence area aligned with their academic and professional interests. Each approved elective should complement the compulsory curriculum rather than repeat Machine Learning, Data Mining, Deep Learning, Robotics, or Natural Language Processing at the same level. Depending on the department’s approved elective list, specialized areas could potentially include Computer Vision, Reinforcement Learning, Big Data, applied AI, or other emerging topics. These examples are illustrative only and are not presented as official elective titles in the attached plan. Each elective should therefore have its own title, prerequisites, learning outcomes, detailed content, teaching strategies, and assessment methods.

Type

Elective Requirement

When

Year four, First semester

Prerequisite

None

This course provides students with the theoretical and methodological foundations of Deep Learning as an advanced extension of previously studied Machine Learning concepts. It focuses on neural-network-based models and on understanding how deep models are designed, trained, regularized, and evaluated. Only the prerequisite Machine Learning concepts required for understanding deep models are briefly reviewed, avoiding repetition of conventional algorithms. The course introduces Artificial Neural Networks, including neurons, layers, weights, biases, activation functions, network architectures, and loss functions. Students study forward propagation and then develop an understanding of backpropagation and gradient-based optimization. Optimization topics include Gradient Descent, Stochastic Gradient Descent, mini-batch training, learning rates, Momentum, RMSProp, and Adam at an appropriate undergraduate level. Problems encountered when training deep networks, including vanishing and exploding gradients, are examined. The course addresses techniques for improving generalization and training stability, including regularization, dropout, batch normalization, early stopping, and data augmentation. A major component covers Convolutional Neural Networks (CNNs) for image and spatial-data processing, including convolutions, filters, feature maps, and pooling. Sequence-modeling foundations are introduced through Recurrent Neural Networks (RNNs), LSTM, and GRU architectures, providing a conceptual foundation for subsequent Natural Language Processing study without duplicating the NLP course. Students are also introduced to transfer learning, pretrained models, and fine-tuning as practical strategies for leveraging previously trained networks. Upon completion, students should be able to design appropriate deep neural architectures, explain the training process, diagnose optimization and generalization problems, select suitable regularization methods, and evaluate Deep Learning models using scientifically appropriate procedures.

Type

Major Requirement

When

Year four, First semester

Prerequisite

Machine Learning

This course introduces nursing students to the fundamentals of Hadith Studies (Ulum al-Hadith) with a focus on prophetic traditions related to health, cleanliness, patient care, medical ethics, and compassion. Students will learn the basic methodology of Hadith authentication and examine selected authentic Prophetic traditions (Ahadith) that guide professional nursing behavior, personal hygiene, mental resilience, holistic healing, and ethical duty toward patients in Saudi healthcare settings.

Type

University Requirement

When

Year four, Second semester

Prerequisite

None

This elective requirement provides an additional opportunity for advanced or applied specialization within Artificial Intelligence. Electives at this level should ideally introduce advanced or emerging topics not sufficiently addressed by the compulsory curriculum and should avoid unnecessary repetition of Machine Learning, Deep Learning, NLP, Robotics, and other core courses. The elective portfolio may evolve as technologies and professional requirements change, but each approved course should have an independent specification including prerequisites, learning outcomes, detailed topics, practical activities, and assessment methods.

Type

Elective Requirement

When

Year four, Second semester

Prerequisite

None

This course introduces the fundamental concepts and methods of Natural Language Processing (NLP) for enabling computers to process and analyze human language. It builds on previous study in specialized AI programming, Machine Learning, and Deep Learning and therefore focuses on applying learned methods to language data rather than reteaching general learning algorithms. The course begins with characteristics of natural language and challenges involving ambiguity, context, morphology, syntax, and semantics. Students then study text preprocessing, including tokenization, sentence segmentation, normalization, stop-word handling, stemming, and lemmatization, with appropriate consideration of language-specific characteristics, including Arabic where relevant. Students learn numerical text representations such as Bag of Words, n-grams, and TF-IDF and then progress to distributed representations such as word embeddings. Major NLP tasks include text classification, sentiment analysis, named-entity recognition, information extraction, and text similarity. Emphasis is placed on formulating each problem correctly, preparing suitable data, selecting models, and evaluating results. Neural approaches are incorporated by briefly connecting previously studied RNN and LSTM concepts to language processing before introducing attention mechanisms and Transformer architectures. Pretrained language models are presented at a level appropriate for undergraduate students. Large Language Models may be introduced conceptually in terms of architecture, capabilities, and responsible use, including limitations such as hallucination, bias, privacy risks, and unreliable outputs. Students also study task-appropriate evaluation metrics and error analysis. Upon completion, students should be able to prepare textual data, select appropriate representations and modeling approaches, construct an NLP pipeline, evaluate its performance, and interpret results and limitations in a technically responsible manner.

Type

Major Requirement

When

Year four, Second semester

Prerequisite

Programming for Artificial Intelligence

This practical course enables students to implement complete NLP pipelines using Python and suitable libraries such as NLTK, spaCy, scikit-learn, Transformers, or equivalent tools. The laboratory focuses on implementation, experimentation, and error analysis rather than repeating detailed theoretical explanations from the corresponding NLP course. Students begin by loading text corpora, handling encoding, cleaning textual data, and implementing tokenization, normalization, stemming, or lemmatization as appropriate to the language and task. They construct representations such as Bag of Words and TF-IDF and use them in tasks such as text classification or sentiment analysis with suitable previously studied Machine Learning models. Laboratory activities may include named-entity recognition, text similarity, or information extraction depending on available resources. Students also work with word embeddings and other vector representations. In the advanced part of the course, students use pretrained Transformer models for inference or fine-tuning on an appropriate task. Emphasis is placed on understanding tokenizers, model inputs and outputs, configuration, and evaluation rather than implementing Transformer architectures from scratch. Students evaluate systems using precision, recall, F1-score, or other task-appropriate metrics and conduct error analysis to identify recurring failure patterns. Arabic NLP is incorporated where appropriate data sets and tools are available, enabling students to examine language-specific challenges such as morphology and orthographic variation. The course culminates in a mini-project beginning with a textual data set and resulting in an NLP model or application that is evaluated, analyzed, and documented in a reproducible manner.

Type

Major Requirement

When

Year four, Second semester

Prerequisite

Programming for Artificial Intelligence - Lab

This course provides students with an opportunity to apply knowledge and skills acquired throughout the program in a real professional environment related to Artificial Intelligence, Data Science, software development, or Information Technology. Training is completed under academic and workplace supervision and follows an agreed plan defining relevant activities and expected outcomes. Depending on the host organization, activities may include data preparation, data analysis, software development, development or evaluation of Machine Learning models, database or cloud-platform work, testing, automation, or technical support. The precise activities depend on the professional context and are not expected to cover every area of the curriculum. The primary purpose is professional practice rather than introduction of a new academic subject. Students are expected to apply suitable development practices such as version control, documentation, testing, teamwork, and responsible data handling where relevant. Students gain experience communicating with supervisors and colleagues, understanding requirements, managing tasks and deadlines, participating in meetings, and reporting progress and technical problems. Where work involves data or AI systems, students must comply with organizational requirements concerning privacy, confidentiality, authorized access, security, and ethical use of information. Training activities are normally documented through a log, portfolio, or equivalent record. Students prepare a final report describing the host organization, assigned tasks, tools and technologies used, skills developed, challenges encountered, and outcomes achieved, together with appropriate professional and academic evaluation. Upon completion, students should demonstrate greater independence in professional environments, connect academic knowledge with practical workflows, understand how technical projects operate in real organizations, and identify competencies requiring further development before entering employment.

Type

Major Requirement

When

Year four, Second semester

Prerequisite

None

This course is the final integrative experience of the Artificial Intelligence program and requires students to implement the project developed and planned in the Introduction to Graduation Project course. The curriculum identifies the introductory project course as the prerequisite. Students begin by reviewing the approved proposal and refining requirements, objectives, or research questions where necessary before executing the planned work systematically. In AI-oriented projects, activities typically include lawful acquisition or collection of a data set, data preparation, establishment of an appropriate baseline, selection of algorithms or models, experimentation, and evaluation using predefined metrics. Students are expected to maintain proper separation among training, validation, and test data, avoid data leakage, and document hyperparameters, experimental configurations, and results to support reasonable reproducibility. Software-, robotics-, or IoT-oriented projects may instead require system design, implementation, integration, testing, and evaluation according to the nature of the problem. Artificial Intelligence should be incorporated only where technically justified rather than as an arbitrary project requirement. Students use version control for source-code management and document major technical decisions, implementation stages, errors, limitations, and risks. Privacy, security, intellectual property, research ethics, and responsible AI requirements must be addressed whenever relevant to users or data. The project culminates in a comprehensive Graduation Project Report, demonstration of the developed system or experimental results, a formal presentation, and a defense before an academic committee. Upon completion, students should demonstrate the ability to integrate technical and methodological knowledge, solve a complex problem systematically, critically evaluate the resulting solution, document their work professionally, and defend their design decisions, methodology, and conclusions.

Type

Major Requirement

When

Year four, Second semester

Prerequisite

Freelance Work

This course aims to introduce students to the concept of freelancing as one of the modern work models that relies on independence and flexibility in providing services or products online or directly. It focuses on equipping students with the essential skills and knowledge needed to start in the freelancing market, advance toward project management, and achieve financial sustainability

Type

University Requirement

When

Year four, Second semester

Prerequisite

None