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network architecture, communication neutral approach, and includes a final AIRE 310 - Machine Learning the concepts learned in this course in allow the designers to achieve
protocols, and application lab in which students address a big AI Concentration and Pattern Recognition their course project to solve any real- energy efficiency and optimal power
characteristics. The course covers the data analytics challenge by applying Core Courses world problem of their choice using consumption while maintaining
most important potential IoT security the concepts taught in the course Credit Hours: 3 deep learning systems. the maximum AI computational
risks and threats and presents both in the context of the Data Analytics Prerequisite: CSC 201 + COE 101 + performance. Furthermore, in this
the general theory and practical Lifecycle. The course prepares the MTT 200 course, the students will learn various
implications for people working in student for the Proven™ Professional AIRE 305 - Artificial Intelligence AI Concentration methods that can improve the design
security in the Internet of Things. Data Scientist Associate (EMCDSA) for Engineers This course will provide Elective Courses schematics of the neural network
certification exam. comprehensive understanding architectures in order to give optimal
EEN 220 - Electric Circuits II Credit Hours: 3 in machine learning and pattern trade-off between recognition
EEN 337 - Analog and Digital Prerequisite: CSC 201 + COE 101 recognition concepts and algorithms.
Credit Hours: 3 Communication During this course, students will AIRE 475 - Self-Driving Cars performance and computational
Prerequisite: CEN 201 This course introduces students to learn how to design and construct cost when deployed on the ultra-low
Credit Hours: 3 broad topics in artificial intelligence Credit Hours: 3 power resource constrained devices.
This course introducing alternating Prerequisite: CEN 320 for engineering students and uses a pattern recognition system, Prerequisite: CSC 201 + CEN 325
current (AC) analysis. It defines data from the medical field as understand statistical and structural AIRE 482 - Natural Language
instantaneous Power, average power Signal analysis: Fourier series examples and use cases. Some of methods, and study different The objective of this course is to Processing
and RMS values, active and reactive representation, properties of Fourier these topics are explored within theories and models such as support provide guided experience in wide
Power. Topics covered include: Three transform, power spectrum, and their own courses later in the vector machines and decision areas of artificial intelligence and Credit Hours: 3
Phase Circuits and Power Distribution Dirac delta function. Signal distortion curriculum, like vision rand deep trees. Additionally, this course will computer vision to student teams Prerequisite: AIRE 310
systems: Configuration of Different over a communication channel. learning elated topics. The aim of the cover nonparametric techniques working on a major design project. Natural language processing (NLP) is
Three phase Systems, Three-phase Bandwidth of typical communication course is to give students a holistic and clustering. By the end of this The projects will integrate various a sub-branch of Artificial Intelligence
Power, Power factor Correction. channels. Principles of modulation: overview of the field and some of its course, students are expected to engineering skills into self-driving that has broad applications in
Magnetically Coupled Circuits: Mutual Amplitude modulation (AM), double bio-engineering applications. The have a fully conceptual and practical car prototype. The projects will the humanities, social sciences,
Inductance, Dot Convention, Energy sideband (DSB), single sideband course begins with an introduction understanding in the provided topics. emphasize problem definition, and hard sciences. The ability to
stored, Ideal Transformers, Three (SSB), vestigial sideband (Television); to artificial intelligence including its The course trains students on using design conceptualization, modeling, automatically harness linguistic and
Phase Transformers. Frequency Angle modulation: frequency history and terminology. Students Python’s SkLearn for implementing fabrication and system integration textual data is a highly valuable skills
Response: Network Functions, Bode modulation (FM), phase modulation explore problem-solving using machine learning systems. in software and hardware aspects. to gain employment in academia,
Plot, Resonance Circuits. Two port (PM); frequency division multiplexing artificial intelligence as a searching, AIRE 410 - Deep Learning This course builds on concepts governmental organizations, and in
networks: Admittance Parameters, (FDM). Sampling, quantizing, and optimization, and filtering problems. learned earlier coursework on vision, corporate sector.
Impedance Parameters and Hybrid Pulse Code Modulation (PCM): Time They are also introduced to the Credit Hours: 3 machine learning, and control to
Parameters. Division Multiplexing (TDM), PAM, knowledge, reasoning, and planning Prerequisite: AIRE 310 introduce students to practical The goal of this course is to provide
PDM, and PPM. using AI logic. Uncertain knowledge self-driving cars technology. Topics a theoretical and methodological
CEN 457 - Data Science and Big Deep learning is a subset of include lane detection, traffic sign introduction to the most popular
Data Analytics CEN 490 - Special Topics in and probabilistic reasoning are also machine learning that focuses on classification, convolutional neural and successful current approaches,
introduced, for example through
Computer Engineering extracting complicated, hierarchical networks (CNN) architectures for tactics, and toolkits for natural
Credit Hours: 3 the use of noise in 2D and 3D data. feature representations from the language processing, with a
Prerequisite: CSC 201 + STT 100 Credit Hours: 3 Finally, students are introduced unstructured data. In this course, self-driving cars behavior cloning, particular emphasis on those made
sensor fusion, localization, planning,
This course provides practical Prerequisite: CEN 325 + Department to learning from examples using students will be familiarized with the and proportional–integral–derivative possible through the use of deep
machine learning algorithms as a
foundation level training that Approval prelude to the next course. While core ideas, underlying mathematics, (PID) control. learning-driven language models
enables immediate and effective The course will introduce selected certain implementations of Artificial and implementation details of the implemented using PyTorch and
participation in big data and other special topics to the students from Intelligence lend themselves to deep learning models, and they will AIRE 325 - Ultra-low Power AI TensorFlow libraries.
analytics projects. It includes an the Computer Engineering stream. Prolog and Matlab, others such as also study the ideas and techniques on Microcontrollers
introduction to big data and the The exact list of topics will be chosen data-driven approaches are much through which they can optimize the
Data Analytics Lifecycle to address by a faculty who has experienced more accessible in Python. Students highly parameterized models, as well Credit Hours: 3
business challenges that leverage in the particular areas of CE by also will learn to develop simple Prolog as the components, such as, linear, Prerequisite: CSC 201 + COE 101
big data. The course provides considering the fact that those topics and Python programs to implement convolutional, and pooling layers, This course provides an overview on
grounding in basic and advanced are not being covered in the other their artificial intelligence systems. activation functions, etc., that makes the fundamental design principles
analytic methods and an introduction courses within the curriculum. up the deep learning architectures. employed in the holistic design of
to big data analytics technology and Furthermore, this course will AI-driven low power microcontrollers
tools, including MapReduce and familiarize students in building and embedded computing systems.
Hadoop. Labs offer opportunities for simple to complex convolutional Energy is always a bottleneck for
students to understand how these neural networks for classification, resource-constrained embedded
methods and tools may be applied to regression, detection and devices, especially to execute
real-world business challenges as a segmentation tasks using well-known computationally expensive tasks.
practicing data scientist. The course deep learning libraries, PyTorch and This course explains how different
takes an “Open”, or technology- TensorFlow. Students will also explore hardware and software schemes

Abu Dhabi University | Undergraduate Catalog 2023 - 2024 Abu Dhabi University | Undergraduate Catalog 2023 - 2024