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.

This course is the introduction and overview of basic computer organization. Topics include Computer arithmetic: binary, hexadecimal and decimal number conversions, binary number arithmetic and IEEE binary floating-point number standard. Basic computer logic: gates, combinational circuits, sequential circuits, adders, ALU, SRAM and DRAM. Basic assembly language programming, basic Instruction Set Architecture (ISA), and the design of single cycle CPU. Students are expected to develop a computer program simulating how to solve a particular problem using concepts in basic assembly language programming.

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.