Page 447 - Undergraduate Catalog 2024-25

447

EEN 220 - Electric Circuits II electronic components, and testing communicate their experience using
and measuring equipment to design presentations and reports.
Credit Hours: 3 a FM transmitter circuit capable
Prerequisite: CEN 201 of wirelessly transmitting audio AIRE 410 - Deep Learning
This course builds on the concepts signals to an FM radio receiver and Credit Hours: 3
and skills acquired in CEN201 Electric communicate their experience using Prerequisite: AIRE 310
Circuits I. It introduces students presentations and reports.
to concepts, techniques, and CEN 490 - Special Topics in This course builds on the
applications of AC Electric Circuits. Computer Engineering concepts and skills acquired in
Topics covered include instantaneous AIRE310 Machine Learning. It
Power, average power and RMS Credit Hours: 3 provides students with an in-
values, active and reactive Power, Prerequisite: Senior Level depth understanding of concepts,
Three Phase Circuits and Power techniques, and applications of
Distribution systems, Power factor This course will include advanced Artificial Intelligence (AI) and Deep
Correction, Magnetically Coupled topics of contemporary interest Learning. Topics covered include
Circuits, Transformers, Frequency in selected areas of Computer Artificial Intelligence, Deep Neural
Response, Resonance Circuits, and Engineering. Particular topics vary Networks, Convolutional Neural
Admittance Parameters. Students from term to term depending on Networks (CNN), Autoencoders,
connect theoretical concepts learned the interests of the students and the YOLO, Generative Adversarial
in the course to practice using specialties of the instructor. Networks (GANs), and Deep
hands-on laboratory experiences Reinforcement Learning. Students
covering AC circuits, power connect theoretical concepts learned
transferred, frequency, and power AI Concentration in the course to practice using hands-
factor corrections.. The course has Core Courses on laboratory experiences covering
a project. In this project, students Implementing Backpropagation
work teams using Multisim and AIRE 310 - Machine Learning using Python, CIFAR10 Classification
testing equipment to design a three- using Keras, and and Region-based
phase power system and explore its and Pattern Recognition Convolutional Neural Networks
simulation and communicate their Credit Hours: 3 (RCNNs) for Stop Sign Detection.
experience using presentations and The course has a project. In this
reports. Prerequisite: CSC 201 + COE 101 + project, students work teams using
MTT 200 PyCharm, Scikit Libraries, Keras, and
EEN 337 - Analog and Digital Tensorflow to design a YOLO and
Communication This course builds on the concepts RCNN Object Detection Networks
and skills acquired in CSC201
Credit Hours: 3 Computer Programming I, and communicate their experience
using presentations and reports.
Prerequisite: CEN 320 COE101 Introductory to Artificial
Intelligence, and MTT200 Calculus II. AIRE430 - Generative AI
This course builds on the concepts It introduces students to concepts,
and skills acquired in CEN 320 Signals techniques, and applications of Credit Hours: 3
and System. It introduces students Machine Learning (ML). Topics Prerequisite: AIRE 310
to concepts, techniques, and covered include data structures,
applications of Analog and Digital training and testing, performance This course builds on the
Communication. Topics covered assessment, classification, and concepts and skills acquired in
include basics of analog and digital regression. Students connect AIRE310 Machine Learning. It
communication, signals and spectra, theoretical concepts learned in the provides students with an in-
analog modulation techniques (AM, course to practice using hands-on depth understanding of concepts,
FM, PM), the transition from analog to laboratory experiences covering techniques, and applications of
digital, baseband transmission using Regression from scratch using generative modeling and deep
digital modulation techniques (PAM, numpy, Classification comparison learning. Topics covered include
PCM). Students connect theoretical using sklearn, Image classification Generative Modeling, Variational
concepts learned in the course to using Keras and pytorch. The course Autoencoders, Generative Adversarial
practice using hands-on laboratory has a project. In this project, students Networks, Text-to-Image Generation,
experiences covering Analog and work teams using PyCharm, Scikit and Autoregressive Models. Students
Digital Modulation Schemes. The Libraries, and PyTorch to design a a connect theoretical concepts learned
course has a project. In this project, deep learning image classifier and in the course to practice using hands-
students work teams using MATLAB, on laboratory experiences covering

Abu Dhabi University | Undergraduate Catalog 2024 - 2025