This course provides an elementary introduction to probability and statistics with applications. Topics include basic combinatorics, random variables, probability distributions, Bayesian inference, hypothesis testing, confidence intervals, and linear regression as well as illustrative examples and simulation exercises from queuing, reliability, and other CS applications. At the end of the course, students are expected to submit an analysis report illustrating the application of probabilistic and statistical techniques on their chosen computing-related topic of interest.

This course provides an introduction to algorithms and complexity at the undergraduate level. The course will cover the most important algorithmic techniques as well as methods for analyzing algorithm performance. The major topics will include algorithms such as brute force, greedy, divide-and-conquer, dynamic programming, and iterative improvement. Algorithm limitations will also be discussed including NP-completeness, approximation algorithms, and randomized algorithms. Students are expected to conduct an analysis report as an evaluation of the algorithms they have chosen to help solve the given real-life problem.

This course is an introduction to mathematical analysis concepts and its applications, which includes differential and integral calculus.   The topics shall include derivatives, mean-value theorem and real-world problems related to differential calculus.  Furthermore, it shall also cover the integral and fundamental theorems of integral calculus and its applications. At the end of this course, the students are expected to create an innovative project that transforms the abstract concepts of calculus into a dynamic and engaging visual experience.

This course provides an introduction to contemporary data-driven approaches in natural language processing. It progresses from simple bag-of-words methods to more sophisticated structural representations that capture how words combine to convey meaning, including language models. At each stage, the course highlights key linguistic phenomena and the most effective computational approaches. In parallel, it introduces machine learning techniques that are particularly important for natural language processing.

The course covers the use of general purpose programming language to solve problems. The emphasis is to train students to design, implement, test, and debug programs intended to solve computing problems using fundamental programming constructs. By the end of the course, students are expected to demonstrate proficiency in Python programming by designing and developing a functional software application. This system must effectively apply core programming concepts discussed in the course, including problem analysis, algorithm design, control structures, data structures, and debugging techniques.

This course provides an overview of the Computing Industry and Computing of profession, including research and Applications in different fields; an Appreciation Computing in different fields such as Biology, Sociology, Environment and Gaming; an Understanding of ACM Requirements; an Appreciation of the history of computing; and Knowledge of the Key Components of Computer Systems (Organization and Architecture), Malware, Computer Security, Internet and Internet protocols, HTML5 and CSS. The student is expected to develop websites and web applications integrating the concepts of HTML/CSS.

This course covers matrices, matrix operations and properties and determinants, vector spaces, linear transformations, eigenvalues and eigenvectors, and diagonalization.

This course begins with the basic concepts and definitions on mathematical models and applications of differential equations to real-world problems. The focus is to teach you how to model the world in terms of differential equations, and how to solve equations and interpret the solutions in computing. Topic includes First-order and higher-order differential equations, along with the methods of solutions and their applications are introduced. Modeling with higher-order, and systems of linear first-order differential equations are also covered. Numerical methods are covered throughout the course. At the end, students learn series solutions of ordinary differential equations and evaluate how it is applied in real life.

The course covers the standard data representation and algorithms to solve computing problems efficiently (with respect to space requirements and time complexity of algorithm). This covers the following: Lists, Stacks, Queues, Trees, Graphs. Thorough discussion of searching, sorting and hashing. At the end of this course, the students are expected to create a narrative video that tackles real-life example of data structure and algorithms.