| Course Code |
Course Name |
Credit hours |
Description |
| A0871103 |
Principles of Renewable Energy |
3 |
Introduction to renewable Energy include Photovoltaic, Wind power, Micro hydropower, Biomass energy, Waste power, Solar thermal power, Geothermal power, Ocean energy (tidal, tide-flow and wave), Ocean energy (OTEC), , Comparison of characteristics and cost of renewables. How we can use the sun, wind, biomass, geothermal resources, and water to generate more sustainable energy. It explains the fundamentals of energy, including the transfer of energy, as well as the limitations of natural resources. Starting with solar power, the text illustrates how energy from the sun is transferred and stored; used for heating, cooling, and lighting; collected and concentrated; and converted into electricity |
| A0110168 |
Digital Literacy and Artificial Intelligence |
3 |
|
| A1321100 |
Sport and Health |
3 |
Defining health and fitness: physical education, health education; the cognitive, emotional, skill-oriented, and social goals of physical education; the history of physical education: ancient, medieval, and modern ages, the Olympics, Athletics in Jordan: nutrition and exercising; athletic injuries: bone, joint , muscle, skin injuries; special exercises for figure deformation; diseases related to lack of exercise: diabetes, obesity, being underweight, back pain, cancer; hooliganism: causes and recommended solutions for hooliganism. |
| A0110167 |
Critical Thinking Skills |
3 |
|
| A0110281 |
Society Health |
3 |
|
| Course Code |
Course Name |
Credit hours |
Description |
| A0341311 |
Networks and Cybersecurity Essentials |
3 |
The course studies the basic of computer networks: types of networks, main devices, Ethernet technology, principles and structure of IP addressing; overview of the common protocols such as: TCP, UDP, HTTP, HTTPS, POP, IMAP, SMTP, DNS, FTP, DHCP; basic security measures and tools: malware, general means of authentication, password-based authentication, physical security, firewall basics; cryptography: symmetric and asymmetric algorithms, hash functions, basics of digital signature and steganography. |
| A0331202 |
Introduction to Programming |
3 |
Sequential execution: program structure, command line arguments, string literals, output, Limerick layout; Program errors: syntactic errors, semantic errors, compile time errors, runtime errors; Types, variables and expressions: string, double and integer types, hard-coded data, assignment statement, arithmetic expressions and associativity, type conversions, parsing input data, integer division, grouping expression terms and long statements layout; Conditional and repeated execution: choice and iteration statements, Boolean expressions, relational operators, program design using pseudo code, lists of command line arguments, comments, standard classes; Control statements nested in loops: declaring variables in compound statements, conditional expression operator; |
| A0334600 |
Ethical and Professional Issues in Computing |
1 |
An overview of ethics, Professional ethics of workers and users in the field of information technology, Cyberattacks and Cybersecurity, Privacy, Intellectual property, Ethical decisions in software development. |
| A0312201 |
Object Oriented Programming |
3 |
Introduction to Object Oriented Programming Concepts using Java language: Classes, Objects, Constructors, Encapsulation: Visibility Modifiers; Packages; Overloading; using this keyword; using static keyword; Array of objects: Store and Process objects in array; Relationships between Classes: Composition, Inheritance: Superclasses and Subclasses, using super keyword, Constructor Chaining, Overriding, Polymorphism, Preventing Extending and Overriding, The Object Class and its toString() Method; Abstract Classes; Interfaces; Exception Handling; introduction to GUI programming. |
| A0311101 |
Discrete Mathematics |
3 |
Logic, relations, functions, basic set theory, countability and counting arguments, proof techniques, mathematical induction, graph theory, combinatorics, discrete probability, recursion, recurrence relations, and number theory. The fundamental mathematical tools used in computer engineering as: sets, relations, and functions; propositional logic, predicate logic, and inductive proofs; summations, recurrences, and elementary asymptotic; counting and discrete probability; undirected and directed graphs; introductory linear algebra, with applications in computer engineering.
|
| A0371201 |
Introduction to Information Technology |
3 |
Basic elements of computing: programming, computer, program, operating environment, data, file; Number systems: decimal, binary, conversion; Describing problem solution using standard flowcharting notation; Linux basics: basic commands, working with files, working with directories, file name substitution, input/output and I/O redirection; Linux shell: overview, programming tools; User-defined commands and shell variables: command files, variables, integer arithmetic; Passing arguments: $#, $#, ${n}; Decisions: exit status, test command, else, exit, elif, Null, && and || constructs; Loops: for, while, until, breaking a loop, skipping commands in a loop; Git: installation and configuration, basic commands, branching. |
| A0110101 |
Mathematics (1) |
3 |
|
| Course Code |
Course Name |
Credit hours |
Description |
| A0374504 |
Computer Vision |
3 |
This course covers an introduction to computer vision including the basics of image composition, camera imaging architecture, feature detection and matching, stereo, motion estimation and tracking, image classification, scene understanding, methods for retrieving depth from stereo images, camera calibration, automatic alignment and tracking, and boundary detection and recognition. In order to understand images, both classical machine learning and deep learning are introduced to deal with different image shapes. |
| A0374605 |
Practical Training |
9 |
|
| A0373505 |
Deep Learning |
3 |
|
| A0374501 |
Natural Language Processing |
3 |
The course introduces the linguistic (knowledge-based) and statistical approaches to language processing in the three major subfields of NLP: syntax (language structures), semantics (language meaning), and pragmatics/discourse (the interpretation of language in context). The course will also cover the applications of NLP such as information extraction, machine translation, automatic summarization, question-answering, and interactive dialogue systems. |
| A0372401 |
Introduction To Data Science |
3 |
Introduction to data science; The basics of Python; Data preparation; Exploratory data analysis; Preparing to model the data; Introduction to machine learning; Data visualization. |
| A0374607 |
Graduation Project (2) |
3 |
|
| A0372201 |
Programming for Data Science |
3 |
|
| A0373501 |
Artificial Intelligence |
3 |
Major research topics in artificial intelligence include problem solving, reasoning, planning, natural language understanding, computer vision, automatic programming, machine learning, etc. In this course, we will study the basic knowledge of understanding artificial intelligence. We will introduce some basic search algorithms to solve problems; representation of knowledge and inference; Pattern recognition and neural networks. |
| A0373405 |
Data Mining and Machine Learning |
3 |
|
| A0374401 |
Big Data |
3 |
An enormous amount of data is now being collected through websites, mobile apps, credit cards, and many other everyday tools that we use on a massive scale. This course will explore the logic behind the complex methods used in this field, as well as how research is modeled on big data with real-life examples. By the end of the course students will be familiar with this field and be able to conduct research design using big data. They will gain the benefits of business data analytics, which includes the use of data, statistical and quantitative analysis, exploratory and predictive models, and evidence-based methods for making business decisions. |
| A0374503 |
Business Intelligence |
3 |
This course explores how business problems can be solved effectively by using operational data to create data warehouses, and then applying data mining tools and analytics to gain new insights into organizational operations. Topics will be covered: the differences between types of reporting and analytics, enterprise data warehousing, data management systems, decision support systems, knowledge management systems, big data and data/text mining. Case studies are used to explore the use of application software, web tools, success and limitations of BI as well as technical and social issues. |
| A0373504 |
Robotics Programming |
3 |
This course introduces robots programming, software modification, and operation, where the robot operating system and a number of tools commonly used in robotics programming with a focus on autonomous mobile robots, where the focus is on how to create a user program to interact with sensors and mobile robot actuators and implement algorithms Motion control. This course is concerned with debugging programs using available tools, testing them using simulation tools, and implementing them on a mobile robot. |
| A0373401 |
Data Engineering |
3 |
In this course, students will get an introduction to the fundamental building blocks of big data engineering, and learns the foundational concepts of distributed computing, distributed data processing, data management and data pipelines. Students will discover how to build an effective data architecture, streamline data processing, and maintain large-scale data systems. |
| A0374606 |
Graduation Project (1) |
3 |
|
| A0373101 |
Computing Systems for Data Science and Artificial Intelligences |
3 |
|
| Course Code |
Course Name |
Credit hours |
Description |
| A0374402 |
Data Visualization |
3 |
This course introduces the student to computer vision algorithms, methods and concepts which will enable the student to implement computer vision systems with emphasis on applications and problem solving. The topics will be covered are: Introduction and Image Sensing, Image Analysis, Edge/Line Detection, Segmentation/Morphological Filtering, Fourier Transform, Feature Extraction/Analysis, Pattern Classification. |
| A0372402 |
Statistics and Probability for Data Science |
3 |
This course includes an introduction to probability and statistics with a focus on data science. The topics covered include fundamentals of probability theory and statistical inference, including: probabilistic models, random variables, useful distributions, expectations, the law of large numbers, the central limit theorem, point and confidence interval estimation, maximum likelihood methods, hypothesis tests, and linear regression |
| A0373406 |
Information Security |
3 |
|
| A0374406 |
Selected Topics in Machine Learning |
3 |
|
| A0374505 |
Selected Topics in Artificial Inteligence |
3 |
The aim of this course is to introduce students to different areas of artificial intelligence, and this is done by presenting new tools and techniques, and various research areas in artificial intelligence. The fields and disciplines that use this science to automate tasks and how to apply algorithms and various tools of artificial intelligence in these disciplines are also discussed. |
| A0343412 |
Cloud Computing & Security |
3 |
Introduction to cloud computing: basic concepts and terminology, essential cloud characteristics; cloud service and deployment models: the cloud service models, the cloud deployment models; cloud-enabling technology: multitenant technology, service technology, virtualization technology; fundamental cloud security: basic terms and concepts, cloud security threats. |
| A0373503 |
Information Retrieval |
3 |
This course covers the components, design, and implementation of textual information retrieval systems and various techniques for building information systems based on text analysis, indexing and retrieval, including the following: text indexing, logical retrieval models, vector space retrieval models, and text extraction. Classifying, evaluating and analyzing text, compression methods, reducing memory space, indexing big data, and the best ways to index data according to size and available memory space. |
| A0374502 |
Pattern Recognition |
3 |
This course introduces the basic principles of pattern recognition algorithms and applications, such as faces, letter recognition. The course covers topics such as, pattern representation, pattern recognition systems, preprocessing and feature extraction, theories of supervised and unsupervised learning, object classification and recognition. |
| A0374405 |
Optimization Algorithms |
3 |
|
| A0342314 |
Protection using Linux |
3 |
Linux basic concepts: file system, commands, utilities, text editing, shell programs and word processing; Linux shells: command line syntax, properties, file name generation, redirection, piping and quote mechanisms; File system navigation: controlling access to files, file and directory naming rules and conventions, handling of files and links; Terminal control: working with vi, monitoring and controlling processes, using command line editing, replacing commands, using backup commands; Control operations: print jobs, network communication, group policy management. |
| A0373407 |
Knowledge Representation and Inference |
3 |
|
| A0374404 |
Selected Topics in Data Science |
3 |
The objective of this course is to introduce students to different areas of data science, and this is done by presenting new tools and techniques, and various research areas in data science. The fields and disciplines that use this science to analyze data and extract knowledge and how to apply algorithms and different tools of data science in these disciplines are also discussed. |
| Course Code |
Course Name |
Credit hours |
Description |
| A0313101 |
Algorithms Analysis and Design |
3 |
Introduction: Asymptotic Behavior, O, Omega , Thata notation, analysis of algorithms complexity, proving algorithm correctness with loop invariant, solving recurrences; Sorting: insertion, quick, merge, heap; Advanced Algorithm Analysis and Design: amortized analysis, dynamic programming; Graph: breadth first search, depth first search, Topological sort, minimum spanning tree, shortest path; Advanced data structures: B-trees; String matching: naive, KMP; NP-Completeness: P, NP, NP-Complete classes, proving NP-completeness. |
| A0312101 |
Data Structures |
3 |
Lists: static allocation, dynamic allocation; Stacks: static implementation, linked implementation, operations, applications; Recursion: applications, program stack; Queues: static implementation, linked implementation, operations, applications; General Trees; Binary Trees; Binary Search Trees: traversal, search, add and delete operations; Files: input, output; Graphs: traversal, adjacency matrix, and adjacency list. |
| A0312401 |
Fundamentals of Databases |
3 |
Database Concepts; Database Design Methodologies; Data Modeling using ER and EER; Database Integrity Constraints; Relational Model: Relational algebra, Relational Calculus; Functional Dependencies and Normalization. |
| A0110201 |
Linear Algebra |
3 |
|
| A0334605 |
Communication and Technical Writing Skills |
2 |
|
| A0311301 |
Digital Logic Design |
3 |
Binary Systems: Digital Computers & Systems Binary numbers, Number Base Conversion: Octal & Hexadecimal Numbers, 1's & 2's Complements Binary codes; Boolean Algebra & Logical Gates: Basic Definitions of Boolean Algebra, Theorems of Boolean Algebra, Boolean Functions Digital Logic Gates, IC Digital Logic Families; Simplification of Boolean Function: Karnaugh Map Method with 3 variable , 4 variable, 5 variable Map. Sum of Products, Product of Sums, Don?t care; Combinational Logic: Integrated combinatorial circuits, Sequential circuits, Flip-flops, registers, counters, memory units. |
| A0332501 |
Introduction to Software Engineering |
3 |
"System Development Methodologies: Software Engineering Processes, Waterfall, Prototype, Incremental, and Spiral, with focus on the Unified Process in its agile form; Principles of Software Engineering: Requirements Elicitation, Validation and Verification; Review of Principles of Object Orientation; Object Oriented Analysis Using UML: Behavioural UML Diagrams Use Case, Sequence, Activity, And State Diagrams; Structural UML Diagrams: Object, Class, and Package Diagrams. |
| A0333203 |
Internet Applications Development (2) |
3 |
|
| A0110103 |
Statistics and Probability |
3 |
|
| A0332202 |
Internet Applications Development (1) |
3 |
|
