Describing statistical data by tables, graphs and numerical measures, Chebychev’s inequality and the empirical rule, counting methods, combinations, permutations, elements of probability and random variables, the binomial, the Poisson, and the normal distributions, sampling distributions, elements of testing hypotheses, statistical inference about one and two populations parameters.

This course is a continuation for Computer Programming 1, which aims to deepen students' understanding of the programming language and being able to write free-errors programs using Java language. Topics covered in the course include introduction to Java programming, defining variables, data types, expressions, control statements, methods, arrays, classes, and objects. This course has a practical lab, which trains student to write programs and solve various problems related to the Java programming concepts covered in this course.

This is the first of two general chemistry courses. It introduces the basic principles of chemistry and shows students how chemists describe matter. It revolves around bonding, the most central concept in chemistry. Material covered includes introduction to chemical calculations, stoichiometry and simple reactions, gases, thermochemistry, atomic structure, the periodic table, types of bonding, liquids and solids.

This course provides an introduction to robotics, a field of study concerned with the design, construction, and operation of robots. The course covers the fundamental concepts and techniques of robotics, including robot kinematics, dynamics, control, and perception. It also includes hands-on experience with programming robots using popular robot development platforms such as ROS (Robot Operating System). Students will gain an understanding of the challenges and opportunities of robotics, and learn to apply robotics techniques to real-world problems

This course introduces students to the basic concepts and technologies that underlie today's Internet. From designing attractive user interfaces using HTML, CSS and JavaScript to creating dynamic backend systems using languages such as PHP or Python, students will gain the skills to create interactive and feature-rich web applications. Through hands-on exercises and hands-on projects, students learn about responsive design, database integration, web APIs, and security issues.

Game theory is the study of decision-making in strategic situations, where the outcome of a player's action depends on the actions of other players. In this course, students will learn about the basics of game theory, including models of cooperation, competition, and conflict resolution. The course will cover topics such as Nash equilibrium, prisoner's dilemma, iterated prisoner's dilemma, bargaining theory, and mechanism design. Additionally, the course will explore applications of game theory in fields such as economics, political science, and computer science

Reinforcement Learning (RL) is an advanced interdisciplinary course designed to provide students with a comprehensive understanding of the fundamental principles, algorithms, and applications of reinforcement learning. RL is a prominent subfield of machine learning that focuses on training agents to make sequential decisions in order to maximize cumulative rewards within dynamic and uncertain environments. Throughout this course, students will delve into the theoretical foundations and practical aspects of reinforcement learning, gaining hands-on experience through a combination of lectures, programming assignments, and real-world case studies

This course is designed to provide students with an in-depth understanding of cutting-edge techniques and emerging trends in the field of Artificial Intelligence (AI). The course covers advanced topics that go beyond the fundamentals of AI, enabling students to explore specialized areas and applications.

Measurement and system of units, vectors, motion in one and two dimensions, particle dynamics and Newton's laws of motion, work and energy, conservation of energy, dynamics of system of particles, center of mass, conservation of linear momentum, collisions, impulse, rotational kinematics, rotational dynamics, conservation of angular momentum.

This course aims to present the principles, techniques, and concepts needed for managerial analysis and decision-making. Topics include management of planning, organizing, influencing, and controlling related to the internal and external environment and issues of ethics and social responsibility. It emphasizes on specific communication and managerial skills for computing and information technology professionals.

This course provides students with the fundamentals and practical applications of reinforcement learning, including the latest techniques used to create agents capable of solving complex problems in fields as diverse as gaming, finance, and robotics. With a strong emphasis on hands-on learning, the course covers topics such as dynamic programming, Monte Carlo and time-difference learning, function approximation, deep Q-nets, and modern policy gradient algorithms. In addition, students will explore related topics such as simulated learning, meta-learning, and more complex formulations of the environment.

This course provides an introduction to geographical information systems (GIS), which are computer-based systems used to collect, store, analyze, and present geospatial data. The course covers the basic concepts of GIS, such as data types, data models, and coordinate systems, as well as various GIS technologies and techniques, such as spatial analysis, map design, and database management. Additionally, the course will cover advanced topics such as remote sensing, web mapping, and geospatial data mining

This course provides an introduction to signal processing, which is the science of analyzing, modifying, and synthesizing signals, such as sound, images, and data. The course covers the basic concepts of signal processing, such as time and frequency domains, linear systems, and sampling, as well as various signal processing technologies and techniques, such as filtering, transforms, and modulation. Additionally, the course will cover advanced topics such as digital signal processing, signal compression, and multi-dimensional signal processing.

English 1 is a theoretical, 3-credit hour university requisite, and a general English Course which is designed to serve all BA and BSc Students of (PTUK) in all faculties. This course aims at developing students’ repertoire of the English language main skills as well as sub-skills through providing them with broad varieties of language patterns, grammatical and structural rules, and vocabulary items that can enable them to communicate meaningfully within ordinary and real-life contexts and situations. This course is also oriented towards equipping students with the skills they need to comprehend texts, contexts, and situations that are related to ordinary and real-life topics. Throughout this course, students will be exposed to a wide and various aural inputs in order to broaden and deepen their skills in listening, judgment, and critical thinking. Students of this course are expected to acquire and practice the skills they need to maximize their capabilities to express opinions about ordinary and real life topics both orally and in a written format, which will help in widening the students’ academic horizon.

This course deals with the methods and foundations of scientific research related to computer science. This course covers a range of topics such as how to write scientific research, how to make use of references attribution to them, and how to document and classify information.

Charge and matter, electric field, gauss's law, electric potential, capacitors and dielectrics, current and resistance, electromotive force and circuits, the magnetic field, ampere's law, faraday's law of induction.

The course aims to introduce students to the best practice in the communications required for careers in information technology and computer science. The course covers written, oral and interpersonal communication. The course is designed to satisfy the writing requirement in computer science and information systems programs.

This course covers the basic concepts of software engineering, its functions and objectives, and the methodologies commonly used in software development. The focus in this course is on life cycle models, project estimation and planning, needs analysis, systems design, construction, testing, maintenance and implementation, software standards and quality. Upon completion, students should be able to identify and apply an appropriate methodology for their programming projects.

Review of digital signal processing, image digitization, human-machine visual system, color, practical and algorithms on images, image enhancement and refining, image transformations, image compression. Focus on the basic principles of image processing. Students will learn to apply the material by implementing image processing algorithms using state-of-the-art software libraries.

The course covers the fundamental concepts of programming, such as variables, data types, control structures, and functions. Students will learn how to write clean and efficient Python code while understanding the principles of problem-solving and algorithmic thinking.In addition, a student will learn how to work with Python's built-in libraries and modules, as well as how to leverage external libraries for tasks such as data manipulation, visualization, and web development (such as NumPy, pandas, matplotlib, and Scikit-learn).

This course introduces students to the fundamentals needed to extract valuable information from large data sets. Students will delve into the basic principles and techniques of data mining, including data preprocessing, pattern recognition, and predictive modeling. Through hands-on projects and real-world examples, students will gain practical skills in using data mining tools and software to discover hidden patterns, analyze trends, and make informed decisions. The course provides students with the knowledge and competence to navigate the complexities of modern data analysis and to use data mining to make informed decisions in a variety of areas.

This course provides an introduction to the field of computer vision, which involves extracting useful information from images and videos. Topics covered include image processing, feature extraction, object detection, tracking, and recognition. Techniques covered include template matching, edge detection, segmentation, clustering, and machine learning-based approaches. Applications of computer vision may include robotics, surveillance, medical imaging, autonomous vehicles.

This course is a continuation of Graduation Project 1, in which the student implements and documents his proposed system analyzed in Graduation Project 1. Therefore, the main objective of the course is to enable the student to engage with various AI projects that are applied in the real world.

This course aims to introduce students to the latest biometric technology and its applications. The course covers the fundamental theories and algorithms for different modality of biometrics as well as how to develop a biometric system. Topics include various biometric systems such as fingerprints, face, voice, iris and other modalities.

This course provides students with an introduction to programming methodologies, design principles, and building mobile applications. Covering topics such as leveraging device features, adhering to industry standards, learning about operating systems, and using software development kits (SDKs), students will delve into the art of mobile app development. Upon completion of the course, Participants will have the skills to create basic mobile applications with the ability to use them to create smart applications.

The students will study various cryptography techniques and their practical applications. Major areas of focus include symmetric cryptography, public key cryptography, and hash functions. Students will use these technologies for tasks such as digital signatures, certificate generation, verification, and authentication. The course will delve into the analysis of cryptographic techniques, evaluating their performance against specific design criteria and application needs. In addition, the cryptanalysis component aims to improve understanding of cybersecurity issues, covering network security protocols, data security, mobile security, and database security.

The course deals with the events of the Palestinian issue through the most important ages from the Canaanites until the year 2021. It focuses on the Islamic conquest of Palestine in the year 15 AH 636 AD, the Crusader torch from 1099 to the liberation of Salah al-Din al-Ayyubi of Palestine in 1187, and it talks about the Ottomans in Palestine from 1516 to 1917. The course is concerned with the Palestinian issue during the British occupation in 1917, until the Nakba in 1948, and the establishment of the occupation state .It deals with the Palestinian resistance and revolutions during 100 years, and Arab-Israeli wars from 1948 to 2021.The course talks about Palestinian Liberation Organization, Palestinian resistance movements and parties, Palestinian Authority and the peace negotiations projects since the 1978 Camp David Accords until 2021.The course talks about attempts to Judaism Jerusalem and Al-Aqsa Mosque since the Palestinian setback in 1967 until 2021, and the issue of Palestinian refugees since 1948. It also anticipates the future of the Palestinian issue.

This course covers systems of linear equations, algebra of matrices and determinants, Cramer's rule, vector spaces, subspaces and their bases and dimensions, internal multiplication spaces, linear transformations and their matrices, eigenvalues and vectors.

This course provides an introduction to the fundamental concepts and techniques of artificial intelligence (AI), including machine learning, natural language processing, computer vision, and robotics. The course covers the main components of AI systems, such as data representation, inference, learning, and decision-making. The course will also examine the social and ethical implications of AI, as well as current research and trends in the field.

This course provides students with basic concepts relevant to computer security and the protection of computer systems and data from threats which may compromise integrity, availability, or confidentiality. The module covers the following topics: Security of the computer; security of networks; security and the Internet; software and hardware security; mobile and database security; and basic cryptography.

The course provides information on big data and cloud computing: its features, characteristics, data sources, applications and value. The course will cover vector modeling, distributed programming using MapReduce, database systems such as SQL and NoSQL, and columnar big data applications. The course focuses more on hands-on experience with storage systems and big data processing on Hadoop, HDFS, YARN, Spark - Spark Streaming and Spark SQL. The course also introduces public clouds such as AWS, Azure, and Cloudera, and solutions for deploying big data applications in the cloud. A practical assignment is mandatory for this course

This course is designed to serve PTUK students in the faculties of Science and Engineering as well as the students of Educational Technology (ET); it offers a broad overview of the English language learning skills in reading, writing, speaking that will enable them to communicate meaningfully in scientific contexts and situations. It also offers a broad variety of scientific language grammatical patterns and vocabulary items that are needed to comprehend scientific contexts and trends. Throughout this course, students will be exposed to a variety of scientific topics, aural input in order to broaden and deepen their critical thinking skills and to help them express opinions about modern scientific topics and problems.

This course aims to introduce the student to the basic concepts of programming language and to give him the ability to write error-free programs using C language. Topics covered in the course include Introduction to programming, defining variables, data types, expressions, control and condition statements, defining functions and passing parameters, arrays. This course has a Practical lab, which trains student to write simple programs and solve various problems related to the C programming concepts covered in this course.

The course aims to introduce students the basics of computer science and information technologies common terminology. Students will learn computer basics and organization, common tools and applications, data representations, introductions to algorithms and programming. The focus of the course is to develop a good understanding of computing and a basic knowledge about how computers are programmed.

This course builds upon the fundamental concepts and techniques of Natural Language Processing (NLP) covered in the prerequisite course, and explores advanced topics in NLP. The course covers state-of-the-art techniques and algorithms used for NLP, including deep learning models for NLP, language generation, coreference resolution, discourse analysis, and information extraction. It also includes hands-on experience with advanced NLP tools and libraries such as TensorFlow, PyTorch, and BERT. Students will gain an in-depth understanding of the current state-of-the-art in NLP research, and learn to apply advanced NLP techniques to real-world problems

This course is an introductory course to entrepreneurship and creativity. Definition and challenges of entrepreneurship, creative thinking to do productive work projects, the link between creative ideas and available opportunities, feasibility studies, writing proposals, funding of projects, introduction of business profile, comparative measures of performance, analysis of business information, new opportunities, business objectives, evaluation of management and personnel, maintaining and strengthening existing business, overcoming weakness, operational plans, impact and management of change, and to link all these skills to computing and information technology applications.

This course introduces the fundamental concepts of programming, problem-solving, and logic thinking. It includes I/O; expressions and arithmetic, selection, iteration; arrays, string handling, functions, scope, recursion and matrices.

The main objective of this course is to provide students with basic knowledge of database systems, their types, how to create and deal with them, and theoretical and mathematical foundations. It covers a range of topics including: concepts, database architecture, database users, database approaches, relational data model, SQL query language, relational model and practical normalization. The practical part of this course focuses on teaching students how to use one of the database design tools.

The course covers a wide range of topics, including supervised learning, unsupervised learning, reinforcement learning, and deep learning. Students will dive into the intricacies of various machine learning algorithms and models, and gain hands-on experience in implementing and fine-tuning these techniques using popular programming languages and libraries such as Python, scikit-learn, and TensorFlow.

This course aims to provide the student with the knowledge and skills necessary to design and develop programs using the object-oriented approach. Topics covered in the course include being-oriented way of thinking, types, methods, messages, transactions, inheritance, replacement and modification, static and dynamic association, polymorphism, perspective and dependence, entities and files, and some operational issues accompanied by examples and case studies.

This course is the culmination of a student's academic journey where knowledge gained through artificial intelligence will be applied to a real-world project of importance. Under the guidance of experienced supervisors, a student will define the scope, objectives and methodologies of the project, and explore the AI technologies that can be used. Throughout the course, the student will conduct in-depth research, collect and analyze data, and develop innovative solutions to complex problems in their chosen field. By integrating machine learning, deep learning, natural language processing or other subfields of artificial intelligence and finally writing a research abstract that presents the project idea and the artificial intelligence techniques to be used. This course lays the foundation for the student's second thesis project.

Computer Numerical Analysis is an intermediate-level course that covers fundamental concepts and techniques in numerical analysis, which involves developing and implementing numerical algorithms to solve mathematical problems. The course focuses on numerical methods for solving nonlinear equations, interpolation, differentiation and integration, and numerical solutions of ordinary differential equations

This course provides an introduction to fuzzy logic, which is a mathematical framework for dealing with uncertainty and imprecision. The course covers the basic concepts of fuzzy sets and fuzzy logic, as well as various fuzzy logic technologies and techniques, such as fuzzy inference systems, fuzzy clustering, and fuzzy control systems. Additionally, the course will cover advanced topics such as neuro-fuzzy systems and evolutionary fuzzy systems

Agent systems are computer systems that are capable of autonomous action in dynamic and uncertain environments. These systems can learn, adapt, and interact with other agents and humans in a variety of domains, such as finance, healthcare, transportation, and entertainment. This course will introduce students to the concepts, techniques, and applications of agent systems. Students will learn about multi-agent systems, intelligent agents, agent architectures, agent communication, and negotiation. Additionally, the course will explore applications of agent systems in various fields such as robotics, game theory, social network analysis, and e-commerce.

The course aims to develop the students’ cognitive abilities and communication skills in Arabic language by introducing Arabic dictionaries, spelling and grammatical errors, and familiarizing them with ancient and modern Arabic literary models including models from the Holy Qur’an.

Functions: domain, operations on functions, graphs of functions; trigonometric functions; limits: meaning of a limit, computational techniques, limits at infinity, infinite limits ;continuity; limits and continuity of trigonometric functions; the derivative: techniques of differentiation, derivatives of trigonometric functions; the chain rule; implicit differentiation; differentials; Roll’s Theorem; the mean value theorem; the extended mean value theorem; L’Hopital’s rule; increasing and decreasing functions; concavity; maximum and minimum values of a function; graphs of functions including rational functions (asymptotes) and functions with vertical tangents (cusps);

This course covers the basics of discrete mathematics which include Theoretical sets, Venn figures, functions, relations, Hamming ciphers, algorithms and figures, Boolean algebra, positional calculus, mathematical rules and models for calculators, and the Torching machine. The course aims to give the student the basics of discrete mathematics, its applications, and its relationship to computer science.

This course is a comprehensive course that focuses on the fundamental concepts, techniques, and analysis of data structures and algorithms. The course provides students with a solid foundation in understanding how data is organized, stored, and manipulated efficiently. Students will learn various data structures such as arrays, linked lists, stacks, queues, trees, graphs, and hash tables, as well as algorithmic design paradigms including sorting, searching, and dynamic programming. Practical implementation and analysis of algorithms will be conducted using the Python programming language.

This course provides an introduction to the interdisciplinary field of cognitive and knowledge science, which studies the nature of human cognition and the ways in which knowledge is acquired, represented, and used. The course covers the fundamental concepts and theories of cognitive psychology, artificial intelligence, linguistics, and philosophy of mind. It also includes hands-on experience with cognitive modeling tools and techniques, such as cognitive architectures and Bayesian networks. Students will gain an understanding of the current state-of-the-art in cognitive and knowledge science research, and learn to apply cognitive modeling techniques to real-world problems.

This course provides a focused study of electronic circuits, with an emphasis on their key role in the design and creation of robots and advanced intelligent systems. This specialized course is designed for students who want to learn the intricacies of electronic circuits to create innovative robotic and intelligent solutions.

this course covers algorithms, design and implementation of modern information retrieval systems. Topics include: search engine design and implementation, text analysis techniques, search models (such as Boolean, vector spatial, probabilistic, and learning-based methods), search evaluation, search feedback, search log analysis, and applications in web information management .

Students perform voluntary work such as donating blood, repairing homes, tourist trails, or holding educational workshops at the university, and the student is committed to training or working for 40 hours.

antiderivatives; the indefinite integral; the definite integral; the fundamental theorem of calculus ; the area under a curve; the area between two curves.Techniques of integration: integration by substitution; integration by parts, integrating powers of trigonometric functions, trigonometric substitutions, integrating rational functions, partial fractions, rationalization, miscellaneous substitution; improper integrals; application of definite integral: volumes, length of a plane curve, area of a surface of revolution infinite series: sequences, infinite series, convergence tests, absolute convergence, conditional convergence; alternating series; power series: Taylor and Maclurine series, differentiation and integration of power series:

Describe the characteristic, structure and function of living cells include cell metabolism, photosynthesis, genetic and cell division and gene expression

An introduction to the concepts and architecture of computer networks using the OSI and TCP/IP models. The course provides the students with a deep understanding of computer networking architecture, focusing on Data Link Layer, Network Layer, Transport Layer, and Application Layer. Topics include Wired LANs; Wide Area Network (WAN) technologies; Wireless LANs; Connecting LANs; Operation of IP Data Networks; LAN Switching Technologies; Virtual-Circuit Networks; Logical Addressing; Internet Protocol; IP Addressing (IPv4/IPv6); routing protocols; address Mapping; Error Reporting; Multicasting; Delivery; Forwarding; Process-to-Process Delivery; Domain Name System; Remote Logging; Electronic Mail and File Transfer; WWW and HTTP.

The course explores the fundamentals of neural networks, including feedforward networks, recurrent networks, and convolutional neural networks (CNNs). Students will learn about the principles of deep learning, backpropagation, optimization techniques, and regularization methods. They will also gain hands-on experience in implementing and training deep learning models using popular frameworks such as TensorFlow and PyTorch. The course covers introduction to various advanced topics in deep learning, including generative adversarial networks (GANs), deep reinforcement learning, and transfer learning.

This course aims to engage the student in the local and global job market by training in local and international IT companies. The training is supervised by an academic and vocational teacher who is the head of training. All academic internships must be approved in advance.

This course focuses on providing students with an understanding of basic concepts in distributed systems and computing. In addition, students gain theoretical and practical expertise from existing platforms and standards in the field. This course focuses on both the theoretical and practical issues underlying distributed systems design/architectures, and the communications between the practical, middleware, CORBA, RMI, XML, and RPC.

This course focuses on providing students with new concepts and display expertise in technical and management skills with access, use and collection of data, and the data subject's legal right to the data. This course examines the ethical, legal, and social responsibilities of the practicing professional IT specialist. Code of Ethics and Professional Conduct will be introduced and discussed through analyzing case studies. Issues of IT Ethics addressed include Computer in the Workplace, Computer Crime, Privacy and Anonymity, Intellectual Property and Globalization.

This course provides an introduction to the principles and practices of Human Computer Interaction (HCI). Students will learn how to design interactive systems that are easy to use and meet user needs. The course covers topics such as user interface design, user-centered design, usability testing, accessibility and emerging trends in HCI.

This course provides a comprehensive understanding of embedded systems in the context of IoT. Students study hardware components, real-time operating systems, and programming techniques. They study microcontrollers, sensors, actuators, and communication protocols that typically include Wi-Fi, Bluetooth, and transport protocols such as MQTT. Also explore the lifecycle of IoT data processing, which includes capturing, storing in the cloud, and analyzing data across various IoT devices. This course focuses on developing an IoT ecosystem that includes all the components that enable businesses, governments, and consumers to connect to their IoT devices. The IoT ecosystem consists of remote controls, dashboards, networks, gateways, data storage, analytics, and security. Students will use the STM32 IoT Discovery Node and Cloud to demonstrate practical IoT applications. At the end of the course, participants will be able to design an IoT solution prototype using sensor data, communication protocol, cloud, security and security risks

Remedial English: The course is a compulsory service course offered for first year students. It is a prerequisite for E1 and it focuses mainly on the language learning skills: listening, speaking, reading and writing. The course is intended to equip the students with basic skills necessary for successful communication in both oral and written forms of the language. In addition to grammar and how to use vocabulary in a meaningful context.