Are you curious about how programs run, multiple tasks are processed simultaneously, and data is stored securely? Through 13 lessons, you will systematically learn the core principles of operating systems, including processes and CPU scheduling, memory management, threads and synchronization, and file systems. By connecting concepts you may have previously memorized individually, you can understand how computer systems operate and build a solid foundation of knowledge essential for development.

This course systematically covers everything from the basic concepts of machine learning to its major algorithms. Starting with data representation, linear regression and classification, and optimization, it goes on to cover decision trees, ensembles, neural networks, unsupervised learning, and reinforcement learning. You will also learn about model generalization, selection, and interpretation, gaining an understanding of machine learning’s core principles and overall workflow.

This course is for those who are familiar with deep learning terminology but feel overwhelmed by how models learn. Across 13 lectures, it connects and explains the key principles, from the fundamentals of linear models and neural networks to backpropagation, optimization, CNNs, RNNs, and transformers. You will build a solid foundation for studying deep learning systematically by understanding model architectures, the learning process, and the factors that affect performance.

Processing actual smart health data held by Daegu Haany University for use in machine learning, and creating customized smart health applications using vibe coding and refined information.

This course aims to understand various security threats occurring in information systems, particularly in internet environments, and to learn core security technologies to protect against them. Focusing on key cryptographic techniques such as symmetric and asymmetric key cryptography, the course is structured to provide a systematic understanding of the overall flow of information security, including the underlying mathematical concepts of cryptography.

Students will learn the fundamentals of digital circuits, including Boolean logic, logic gates, circuit simplification, state machines, and Register Transfer Level (RTL) design. Additionally, students will learn design methods using Verilog.

This is an introductory lecture designed to help you understand the core concepts of non-invasive EEG-based Brain-Machine Interfaces (BMI) and the process of Motor Imagery-based signal processing. It provides an easy-to-follow explanation of the entire workflow, from the basic principles of EEG signals to Motor Imagery experiments, data processing, and how actual BMI systems operate.

You will develop the ability to define problems based on data and clearly explain the rationale and decision-making process behind them. Additionally, rather than focusing solely on the performance of a single model, you will acquire a pipeline-oriented mindset that evaluates the completeness and reliability of the entire machine learning workflow. Furthermore, you will strengthen your problem-solving skills by tracing back the causes of errors when they occur and deriving improvement directions, and through end-to-end project experience, you will acquire practical ML pipeline capabilities that can be immediately applied in real-world settings.