Course Aim: This course prepares students to gather, describe, and analyze data, and use advanced statistical tools to make decisions on operations, risk management, finance, marketing, etc. Analysis is done targeting economic and financial decisions in complex systems that involve multiple partners. Course contents: Introduction, Data Collection, Data Sampling, Data Preprocessing, Data

Course Aim: This course enables students to explore the realm of big data analytics, develop essential skills for real-world applications and delve into the analysis of big data frameworks like Hadoop and NoSQL focusing on efficient storage and processing to generate valuable analytics. In this course, students will learn the design of algorithms tailored for solving data-intensive problems using

Course Aim: This course explores the fundamentals of communications and computer networks. Students will learn to identify various communication devices and network technologies to be able to design and manage local networks effectively. Additionally, they will delve into local computer networks and their applications, discovering how computer networks play a crucial role in diverse sectors of

Course Aim: The course aim is to provide a solid background in the fundamentals of 1D and 2D digital processing. The course covers many of the major topics in the field, including audio and image representation, 1D and 2D linear systems theory and Fourier analysis, digital filtering, image registration, and image segmentation. Course contents: Introduction to 1D and 2D signals processing such as

Course Aim: To introduce the student to the emerging field of quantum computing from the point of view of CS. The course aims to provide the necessary principles to grasp the essence of quantum computing while using the ‘language’ of computer scientists. The intense focus for the course is on the systems and application aspects. Course contents: Principles: double slit experiment, quantum state

Course Aim: The main aim of the course is to develop students’ knowledge about the fundamentals of digital signal processing (DSP) systems and digital filters design. Course contents: Introduction to Signals and Systems, Classification of signals and mathematical representation of some common signals, System properties, Linear time-invariant systems, Convolution and Correlation., Laplace transform

Course Aim: The course aims to establish solid theoretical groundwork for understanding computational models and compiler design. It encompasses the fundamental principles of formal languages and automata theory, essential for constructing compilers. The course covers three fundamental areas of computation theory: automata theory, theory of computability, and complexity theory. Additionally, it

Course Aim: This course aims to introduce the main concepts of Artificial Intelligence. It allows students to understand the importance of AI and its related fields. The course enables students to know different knowledge representation techniques. At the end of the course, students should master different search and control strategies. Course Contents: Introduction to AI, Uninformed Search

Course Aim: This course aims to provide students with biomedical background an introduction to machine learning with biomedical data. The course covers some of the main models and algorithms for biomedical data clustering, classification and regression. Topics will include handling uncertain medical data using linear and logistic regression, regularization, probabilistic (Bayesian) inference

Course Aim: This course aims to study propositional and predicate logic and their applications in Computer Science, get students acquainted with mathematical thinking, learn methods of proving theorems in Computer Science, study and implement graph algorithms, and weigh the outcomes of the course through its use in practical applications in different computer science branches Course contents

Course Aim: The objective of this course is to provide the freshman students with basic concepts and theorems in calculus of single variable, focusing on differential calculus and introducing integral calculus. Course contents: Functions: polynomials, rational, trigonometric, and their domains – Graphical representation of functions: shifting and reflecting functions – Composite functions inverse

Course Aim: The objective of this course is to provide freshman students with the basic concepts and theorems of differentiation. The basic topics of two and three-dimensional analytic geometry are also introduced. Course contents: Elementary functions with emphasis on trigonometric, hyperbolic functions and their inverses – Theorems and techniques of differentiation – Limits – L’Hôpital’s rule

Course Aim: This course continues with the study of calculus focusing on integral calculus and its applications. It also extends the idea of integration to multivariable functions. The course provides a basic introduction to series and power series expansion. Course contents: Integration techniques (integrate by substitution, integrate with partial fractions, integrate by reduction, use

Course Aim: This course introduces the basic concepts of probability and statistics and their applications. Course contents: Statistical experiments, sample space, events, operations on events, Combinatorial analysis: Permutations, Combinations and Counting rules, Definition of the probability and probability axioms, Conditional probability, Independence of events and Bayes theorem, Definition of

Course Aim: The course introduces Ordinary Differential Equations (ODEs) and their classification. It also presents different analytical and numerical methods for solving first and second order ODEs. It also explains the numerical methods used for solving linear algebraic equations and for performing differentiation, integration, interpolation, and curve fitting. Course contents: Introduction to

Course Aim: This course presents the fundamentals of linear algebra and its applications with emphasis on solving linear systems of equations, vector spaces and the eigenvalue problem. Course contents: Solving a system of linear equations – Vectors – Matrices (operations, matrix equations, matrix inverse) – Determinants – Linear Transformations (maps) – Vector spaces and subspaces – Orthogonality