Data Structure CSE203
This course covers elementary data structures for computer programming, including array, linked list, stack, queue, heap, trees, hash, graph, and basic sorting algorithm. Students will understand basic data structures and the rationale behind them. Be able to select (or design) efficient data structures to solve a given problem.
Discrete Mathematics CSE202
The purpose of this course is to provide essential mathematical tools for those studying Computer Science. In this course, students should learn how to prove and analyze the algorithms based on Discrete mathematics. The course covers the following topics: Set, Relation, Function, Discrete Probability, Logic, Proof, Graph, and more.
Object-Oriented Programming CSE201
Object-oriented programming is one of the programming paradigms that involves designing and programming centered around objects. In this course, you will learn the concepts of object-oriented programming through C++, one of the languages that supports object-oriented concepts, and understand how to apply these concepts through project execution.
Digital Logic Design EE201
Fundamentals of gate-level digital system design and digital programming language (VHDL) are covered in this course. Students will participate in a term project to design and analyze a practical digital system. This course provides fundamental knowledge about theories and designs of digital logics.
Computer Architecture CSE305
This course covers the fundamental structure and operating principles of computer hardware systems. The lectures will address topics such as Instruction Set Architecture (MIPS), processors, cache memory, virtual memory multi-core architecture, and more.
Computer Algorithms CSE301
This course aims to introduce essential algorithms for those studying Computer Science.
In this course, students will learn how to design algorithms for specific problems and how to evaluate and analyze the algorithms. The course covers the following topics: complexity, greedy algorithm, divide-and-conquer, dynamic algorithm, graph algorithm, NP-completeness, and more.
System Programming CSE306
This course is intended to provide the basic knowledge required to implement interactive and efficient software in modern computer systems. This course covers several technical topics, which includes data representation, assembly languages, compile, linking, cache memory, memory hierarchy, virtual memory, I/O subsystems, process/thread, and synchronization. For this course, we will have a couple of assignments/project for interesting application programming on Raspberry PI by yourself.
Operating Systems CSE304
This course introduces the basic concept of operating systems, which includes process management, memory management, file systems, and I/O management, and provides a detailed explanation of how those concepts are designed and implemented in modern operating systems. In addition to this, this course provides a set of comprehensive programming homeworks and laboratories to help students improve practical system programming skills in UNIX-like operating systems (e.g., GNU/Linux).
The goal of this course is to provide an introduction to the internal design and operation of modern operating systems.
Introduction to Machine Learning CSE302
This is the course dealing with the introduction of machine learning. This course gives an overview of classification, linear/logistic regression, statistical learning theory, boosting, support vector machines, neural network, hidden Markov models, Bayesian networks, Markov model, and so on.
Introduction to Deep Learning CSE303
This course provides the introductory materials for deep learning, which is a machine learning methodology that learns multiple layers of non-linear representations. This course will also cover some of its applications to computer vision and natural language processing.
Reinforcement learning CSE402
This course covers the fundamentals of reinforcement learning, deep reinforcement learning algorithms, and recent advancements in the field. Additionally, we will implement various deep reinforcement learning algorithms using OpenAI Gym and TensorFlow.
Computer Networks CSE403
This course covers design principles, control algorithms, and basic/advanced networking techniques that form the basis of Internet and network systems. In addition, we take a detailed look at the core functions and operating principles of each network layer such as transport, routing, medium access control, etc.
Introduction to Computer Vision CSE404
This course covers basic algorithms for image manipulation, enhancement, segmentation and feature extraction in time and frequency spaces as well as camera calibration and augmented reality. The course also proceeds to image filtering with some indication of the role and implications of Fourier space, and more advanced characterizations and feature detection techniques such as edge and corner detections, together with treatment of colour images and 3D volumes. The course allows students to explore a range of practical techniques, by developing their own simple processing functions either in a language such as C++ and/or by using software libraries and tools such as Matlab.
Introduction to Databases CSE401
This course aims to cultivate an understanding of the fundamental concepts of databases and the ability to utilize them. Students will learn the theories related to the components of database systems, data modeling, database design, and techniques for constructing and utilizing databases. Additionally, the course covers the foundational theories of emerging technologies such as big data and data mining. Upon completing this course, students will be able to design and develop database applications.
Introduction to Computer Security CSE405
This course covers fundamentals of computer/information security, including cryptography basics, software security, OS security, database security, network security, web security, and more. As an introductory computer security course, students are expected to understand the principles and fundamentals of computer security from various topics. In addition, they gain knowledge and skills to build secure computer systems through this course.
Digital Signal Processing EE401
This course covers various techniques of modern digital signal processing used in a wide range of applications. It focuses on reviewing the mathematical foundations of discrete-time signal analysis, studying the theory and implementation of fast Fourier transform algorithms, and the design and implementation of digital filters.
Digital Image Processing EE402
This course is designed to introduce students to the key concepts and techniques of digital image processing and manipulation using algorithms. Topics include image filtering, enhancement, restoration, reconstruction, segmentation, and morphological processing, with practical implementation in MATLAB.
Prerequisite 1
Prerequisite 2
Recommended