Page 593 - Undergraduate Catalog 2026-27

592 593

NoSQL and MongoDB. data encompass various methods/ network devices to maintain integrity, in the current Software Engineering Bachelor Machine Learning, Deep Neural
techniques with many challenges confidentiality and availability of data curriculum such as Software Networks, IoT, Microcontrollers,
ITE 414 - Introduction to to be considered. In this course and. The course concludes upon Testing, Software Maintenance, Embedded Systems, TinyML. Students
E-commerce students will have a comprehensive the topic of legal and ethical aspects Software quality and Software of Science connect theoretical concepts learned
Credit Hours: 3 overview of the Big Data ecosystem of computer security including Measurement. Selected Topics in in Artificial in the course to practice using hands-
Prerequisite: Junior Level and its value chain. They will learn cybercrime, intellectual property, Software Engineering covers the on laboratory experiences covering
how to identify and evaluate different privacy and ethical issues. update to date and key research Intelligence TinyML Models Training and Testing,
With the rapid growth of the Internet, Big Data challenges. They will gain areas of selected topics in software Deployment of TinyML Models to
commerce on the web has been the essential theoretical and practical CSE 410 - Mobile Device Security engineering. It provides many Microcontrollers, and Wake-Word
a significant part of the revenue knowledge required to evaluate Credit Hours: 3 methods and practices used in Engineering Detection Application. The course has
stream for companies. This course Big Data quality requirements and Prerequisite: CSC 305 the different phases of Software a project.
will develop a comprehensive design and implement business Development Life Cycle, comprising
understanding of the main business solutions using Big Data methods This course focuses on how to secure requirements analysis, requirements AIN310 - Machine Learning and In this project, students work teams
concepts and technical facets related and technologies. The course mobile devices, i.e., any device that specification, software design, Pattern Recognition using Arduino IDE , TensorFlow Lite,
to e-commerce. It starts by defining walks the students through the cannot be not classified as a desktop implementation, maintenance, and PyCharm, Arduino Nano 33 BLE
e-commerce and identifying its most Big Data process starting from or a server, and the significant threats process. The course topics contain Credit Hours: 3 Sense, Sensors, and Actuators to
prevalent features, then it moves data acquisition, and moving to affecting the services delivered over state of the art research articles from Prerequisite: CSC201 + COE101 + design a TinyML-based Model and
to identify several e-commerce storage, processing, analytics and the mobile infrastructure. The main the software engineering discipline. MTT200 Deployment and communicate their
business models. Students will learn visualization. Throughout the course security principles incorporated in The course will equip students with experience using presentations and
how to identify and analyze all the the students will learn the tools the design of several generations the necessary skill to conduct pioneer This course builds on the concepts reports.
and skills acquired in CSC201
major elements of an e-commerce and frameworks required to handle of mobile networks is overviewed. research in software engineering AIN410 - Deep Learning
business model and will learn how to Big Data. Finally, by completing Various security models will be and apply these skills in their future Computer Programming I,
design a comprehensive e-commerce this course, students will be able explored including the main popular research activities. COE101 Introductory to Artificial Credit Hours: 3
Intelligence, and MTT200 Calculus II.
business plan. The course introduces to develop a Big data strategy and mobile device platforms such as: Prerequisite: AIN310
the students to the IT infrastructure implement a Big data business iOS, Android and Windows Phone. In The course emphases on traditional It introduces students to concepts, This course builds on the concepts
techniques, and applications of
of e-commerce and helps the solution using the techniques, addition, the course teaches students and evolving methods and paradigms
students understand the technical methods, and technologies about the security of mobile services, of software engineering and helps Machine Learning (ML). Topics and skills acquired in AIN310 Machine
Learning and Pattern Recognition.
challenges involved in developing an introduced throughout the course. such as VoIP, text messaging, WAP students to understand and use covered include data structures, It provides students with an in-
training and testing, performance
e-commerce web presence. Finally, and mobile HTML. Students will research skills in the software
the students will learn the process of CSE 400 - Network Security and become familiar with various tools engineering area. assessment, classification, and depth understanding of concepts,
regression. Students connect
techniques, and applications of
planning, designing, and developing Forensics that are used to recover cell phone
an e-commerce web presence and Credit Hours: 3 data, and the type of extractions, and theoretical concepts learned in the Artificial Intelligence (AI) and Deep
Learning. Topics covered include
will apply the learned concepts and Prerequisite: CSC 305 will be able to analyze the results by course to practice using hands-on Artificial Intelligence, Deep Neural
laboratory experiences covering
technologies to design and develop a diving deep within the file systems of
working prototype of an e-commerce This course provides the students mobile devices. Students will engage Regression from scratch using Networks, Convolutional Neural
Networks (CNN), Autoencoders, YOLO,
numpy, Classification comparison
website. The course also tackles the opportunity to examine in forensic acquisition and analysis of
topics related to online payment network-based attacks and whether mobile computing devices, specifically using sklearn, Image classification Generative Adversarial Networks
(GANs), and Deep Reinforcement
methods and security to enrich the originating from outside the iOS, Android, and Windows Phone using Keras and pytorch. The course Learning. Students connect
has a project. In this project, students
learning experience. enterprise (Internet) or from the local devices. work teams using PyCharm, Scikit theoretical concepts learned in the
LAN. In addition, this course provides
ITE 442 - Data Science and Big an introduction to the methodology SWE 490 - Selected Topics in Libraries, and PyTorch to design a course to practice using hands-on
Data Analytics and procedures associated with Software Engineering deep learning image classifier and laboratory experiences covering
Implementing Backpropagation
Credit Hours: 3 digital forensic analysis in a network Credit Hours: 3 communicate their experience using using Python, CIFAR10 Classification
presentations and reports.
Prerequisite: (SWE 201 or CSC 201) + environment. The course will provide Prerequisite: 90 Credit Hours using Keras, and and Region-based
STT 201 the students with the methods AIN325 - Edge AI Convolutional Neural Networks
and ways to protect, detect, and Software Engineering curriculum
The Big Data course focuses on defend the enterprise network cover fundamental principles in Credit Hours: 3 (RCNNs) for Stop Sign Detection.
managing and processing massively from such attacks. Students will different area such as Software Prerequisite: CSC201 + COE101 The course has a project. In this
large, versatile, continuous, and also learn about the importance of development life cycle, Object This course builds on the concepts project, students work teams using
heterogeneous data retrieved from network forensic principles, legal Oriented design patterns, Software and skills acquired in CSC201, and PyCharm, Scikit Libraries, Keras, and
different sources including, for considerations, digital evidence requirements and specification and COE101. It introduces students Tensorflow to design a YOLO and
instance, sensors, social media, web controls, and documentation of many other areas. to concepts, techniques, and RCNN Object Detection Networks and
applications, ERP systems, mobile forensic procedures. The practical applications of Machine Learning communicate their experience using
applications, transportation systems, component of this course will The main purpose of this course (ML), Deep Learning, and Internet of presentations and reports.
and others. Acquiring, storing, provide the students with the skills is to study Software Engineering Things (IoT). Topics covered include
processing, and visualizing this to install, troubleshoot and monitor related topic that are not included
Abu Dhabi University | Undergraduate Catalog 2026 - 2027 Abu Dhabi University | Undergraduate Catalog 2026 - 2027