Page 439 - Undergraduate Catalog 2024-25

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Matrices. Engineering applications of with emphasis on the presentation PC and required software.
linear algebra are incorporated using and interpretation of experimental
Math software available. data. • Experiments will be performed
as shown in the lab syllabus. All
MTT 205 - Differential Equations PHY 201 - Physics and labs will include an introductory
Engineering Applications II lecture followed by completion of
Credit Hours: 3 the laboratory assignment. Before
Prerequisite: MTT 200 Credit Hours: 3 students leave the lab, they must
Co-requisite: MTT 204 Prerequisite: PHY 102 request the instructor s review of
The course aim is to provide The course is intended to provide their data and sign it. Signed raw
engineering students with some engineering and computer data sheets must be attached to
standard methods to solve first science students with sufficient reports when they are submitted.
order Separable, Exact, Linear and understanding and knowledge of Student cannot receive a lab report
Bernoulli differential equations. physics concepts in Electricity and grade without an original raw data
Construct mathematical models of Magnetism that can be relevant to sheet signed by their instructor.
simple physical systems. Solve higher their field of study. The course is CSC 201 - Computer
order linear ODE’s with constant divided into two parts; Electricity and Programming I
coefficients. Solve ordinary linear Magnetism. The topics covered are;
differential equations using infinite electric field, Gauss’s law, electric Credit Hour: 3
series and Laplace transform. Solve potential, capacitance and dielectrics, Prerequisite: MTT 101 or Higher
systems of differential equations. current and resistance, direct current
circuits, magnetic fields, sources The main objective of this course is to
PHY 102 - Physics and of magnetic field, Faraday’s law, provide students with the logic and
Engineering Applications I inductance, and alternating current tools required to develop scientific
Credit Hours: 3 circuits. software programs in MATLAB.
MATLAB is a matrix based language
Prerequisite: MTT 102 Taken Simultaneously with PHY 201L that is commonly used for scientific
The course aim is to provide (1credit hour) prerequisite PHY 102 + and engineering computing. MATLAB
engineering and computer science PHY 201 Co-requisite. has a rich set of toolboxes for a wide
students with clear understanding PHY 201L - Physics and range of applications in science
of the basic concepts of physics. Engineering Applications II and engineering. The material for
The course is divided into two parts: Laboratory this course includes: Introduction
Mechanics, and Waves. The topics to Matlab Programming concepts,
covered are; Units, Vectors and Credit Hour: 1 Control Structures (loops and
Scalars, Kinematics, Newton’s laws of Prerequisite: PHY 102 conditions), Functions, Arrays and
Motion, Work and Energy, Oscillatory Co requisite: PHY 201 Object-Oriented programming.
Motion, Waves Motion, Sound COE 101 - Introductory Artificial
Waves and Superposition of Waves. This course is designed to help Intelligence
Taken simultaneously with PHY 102L students develop the ability to
(1credit hour) prerequisite MTT 102 + perform scientific experiments and Credit Hours: 3
PHY 102 Co-requisite. to enhance their understanding of Prerequisite: STT 100
theoretical material presented in
PHY 102L - Physics and Phy201 (Electricity and Magnetism) This course builds on the concepts
Engineering Applications I by performing landmark experiments and skills acquired in STT100 General
Laboratory with emphasis on the presentation Statistics. It introduces students
and interpretation of experimental to concepts, techniques, and
Credit Hour: 1 data. applications of Artificial Intelligence.
Prerequisite: MTT 102 Topics covered include Artificial
Co-requisite: PHY 102 • The student will be required to Intelligence Termonologies, Data
make extensive use of computer-
This course is designed to help generated graphs and tables Preprocessing, Supervised Learning
students develop the ability to for displaying and analyzing ( e.g., Regression and Classification),
perform scientific experiments and experimental data. This will be AI Performance Evaluation and Bias,
to enhance their understanding accomplished using Excel or other Neural Networks, Convolutional
of theoretical concepts presented spreadsheet programs of comparable Neural Networks, Clustering,
in Physics I course (PHY102) by capability. To accomplish this, each Reinforcement Learning, Ethics in AI
performing landmark experiments laboratory station is equipped with a and AI Strategy. Students connect

Abu Dhabi University | Undergraduate Catalog 2024 - 2025