Yr.

Subject

Key Topics

Descriptions

1^st Semester of 1^st Year

1/1

 ★ Calculus I

• - Function, limit, continuity
• - Differentiation, integration
• - Sequence, series

Covers differential calculus with applications in science and engineering. Students learn about limits, derivatives, rates of change, and optimization. The course emphasizes problem-solving and modeling real-world systems. Concepts are connected to programming and robotics contexts, such as motion control. This math foundation is critical for later AI and robotics algorithms.

1/1

★ Introduction to Computer Science

• - Introduction to Peripheral devices
• - Introduction to S/W, H/W
• - Introduction to Network, Security.

This course introduces students to the core principles of computer science and computational thinking. It explores the role of algorithms, data, abstraction, and problem-solving in building software systems. Students gain exposure to how computers operate and how programming languages execute logic. They will engage in hands-on exercises and learn basic programming constructs. The course lays the groundwork for deeper study in computer science and intelligent systems.

2^nd Semester of 1^st Year

1/2

Calculus II

• - Polar coordinate, vector
• - Vector function, partial derivative

• - Multiple integral, vector calculus

Continues from Calculus I, focusing on integration and sequences. Topics include techniques of integration, applications of integrals, series, and convergence. Students use these tools to model accumulation, motion, and control in engineering problems. The course connects theory to computational simulations. It builds the mathematical skills needed for machine learning and control systems.

1/2

★ Programming I

-Python/Java

• - History of computer language
• - C/Python language
• - Loop
• - Function

Focuses on teaching students fundamental programming concepts using a high-level language like Python or Java. Topics include variables, loops, conditionals, functions, arrays, and input/output. Students complete hands-on labs and build simple applications. The course emphasizes algorithmic thinking, code clarity, and debugging skills. It serves as a prerequisite for more advanced software courses.

1/2

★ Discrete Mathematics

• - Sets and logic, function and sequence
• - Counting methods, graph theory
• - Trees and network model
• - Boolean algebra and automata

Explores the mathematical structures that underpin computer science. Topics include logic, proofs, sets, functions, relations, graphs, and combinatorics. Students gain skills in reasoning, abstraction, and formal notation. The course provides a theoretical foundation for algorithms, AI, and robotics. Problem sets help students translate abstract math into computational logic.

1^st Semester of 2^nd Year

2/1

Linear Algebra

• - Vector space, subspace
• - Eigenvalue, eigenvector, orthogonality

• - Determinant, quadratic form

Teaches vectors, matrices, transformations, eigenvalues, and eigenspaces. Applications include image processing, robotics, and neural networks. Students perform matrix operations and study geometric representations. The course supports 3D modeling, computer graphics, and deep learning. It’s crucial for representing and manipulating data in AI systems.

2/1

Differential Equations

• - Separable and exact ODE
• - 1st and high-order linear ODE
• - Systems of ODE

• - Laplace transform

Introduces methods for solving ordinary differential equations (ODEs) and their applications in engineering and computer science. Topics include first-order and second-order equations, systems of differential equations, and Laplace transforms. Students learn analytical and numerical solution techniques. Applications emphasize modeling physical systems, including robotic motion and control systems. This course provides essential mathematical tools for later courses in AI, control theory, and autonomous systems.

2/1

★ Programming II

- Object Oriented Programming

• - Classes and objects
• - Inheritance
• - Polymorphism

• - Encapsulation

Introduces object-oriented programming and deeper software design principles. Students learn about classes, inheritance, polymorphism, and encapsulation. Projects involve designing interactive applications and applying design patterns. Emphasis is placed on modularity, code reuse, and maintainability. This course prepares students for advanced programming and software engineering.

2/1

Digital Logic

• - Logic gate
• - Boolean algebra

• - Complex logic circuit

This course introduces the principles of digital logic design, which form the foundation of computer hardware systems. Students learn about basic logic gates (AND, OR, NOT, NAND, NOR, XOR), Boolean algebra, and logic minimization techniques. The course covers the design and analysis of combinational and sequential logic circuits, including multiplexers, decoders, flip-flops, and counters. Emphasis is placed on designing and simulating complex digital circuits using truth tables, Karnaugh maps, and circuit design tools. This course lays the groundwork for understanding computer architecture, embedded systems, and digital electronics.

2^nd Semester of 2^nd Year

2/2

★ Data Structure

• - Array
• - Stack, Queue
• - Linked list
• - Tree
• - Graph

Covers arrays, stacks, queues, linked lists, trees, heaps, hash tables, and graphs. Students implement each structure from scratch and evaluate memory and runtime trade-offs. Emphasis is placed on the choice of structure based on use case. Projects may include implementing a text editor or caching system. This course is essential for understanding program efficiency and design.

2/2

Probability and Statistics

[확률과 통계]

• - Probability, random variable (RV)
• - Discrete & continuous RV
• - Function of RV

• - Normal distribution and basics of statistics

Covers probability theory, distributions, expectation, and hypothesis testing. Students analyze data and build statistical models. The course includes regression, variance analysis, and estimation. Applications include predictive analytics and AI uncertainty modeling. It builds the foundation for machine learning and robotics sensing.

2/2

Web Programming

• - HTML/CSS
• - JavaScript
• - Server-side scripting

Covers the fundamentals of web development using HTML, CSS, and JavaScript. Students learn front-end and back-end programming, including server-side scripting, databases, and RESTful APIs. Emphasis is placed on responsive design, web accessibility, and security best practices. Projects include building dynamic websites and full-stack applications. The course provides practical skills for developing interactive web services and tools.

2/2

System Programming

• - Processor architecture
• - Assembly language

• - Advanced procedure & Memory management

Dives deep into the intricate relationship between software and hardware. You'll explore how programs interact with the operating system, managing processes, memory, and I/O. Key topics include low-level programming in C, shell scripting, system calls, and inter-process communication. We'll also delve into concurrency and network programming, equipping you with the skills to build robust and efficient systems.

2/2

Introduction to Robotics

• - Sensors and Actuators
• - Kinematics
• - Control Systems
• - Autonomous Navigation

Introduces robot design, sensors, actuators, and control systems. Students program robots to navigate, avoid obstacles, and perform tasks. Emphasis is placed on hardware integration and system feedback. Labs involve simulation or physical robots. The course bridges mechanical systems with intelligent control.

1^st Semester of 3^rd Year

3/1

★ Algorithm

• - Efficiency, analysis, and order
• - Divide and conquer
• - Dynamic programming
• - Greedy algorithm

• - Computational complexity

Introduces algorithm design techniques such as divide-and-conquer, greedy methods, dynamic programming, and backtracking. Students analyze time and space complexity using Big-O notation. Topics include sorting, searching, graph traversal, and shortest path algorithms.

Projects involve solving real-world problems using algorithmic approaches. Prepares students for technical interviews and advanced AI coursework.

3/1

Signal and System

• - Fourier series, filter design
• - Continuous-time Fourier transform
• - Discrete-time Fourier transform
• - Laplace transform, Z transform

Explores essential theories and applications in modern engineering. Students will master Fourier series and filter design, alongside Continuous-time and Discrete-time Fourier Transforms for analyzing signals in various domains. The powerful Laplace and Z transforms will be covered for comprehensive system analysis. This course provides a robust framework for understanding and manipulating signals and systems across diverse engineering fields.

3/1

★ Machine Learning

• - Popular python modules
• - Regression, classification
• - Neural network

Teaches algorithms for learning from data, including regression, classification, clustering, and neural networks. Students explore supervised and unsupervised learning techniques. Projects involve real datasets and performance evaluation. Tools like scikit-learn and TensorFlow are used. The course is foundational for modern AI.

3/1

Database

• - ER modeling
• - SQL
• - Transaction management

• - Database tuning

Introduces relational databases, ER modeling, SQL, and normalization. Students learn to design schemas and query databases. Topics also include transactions, indexing, and distributed databases. Projects involve creating and managing databases for real applications. It’s essential for data-driven AI systems and back-end development.

3/1

Computer Architecture

• - CPU design
• - Pipelining

• - instruction-level parallelism

Provides a foundational understanding of how computers are designed and operate at a fundamental level. You will delve into the organization and design of computer systems, from the basic logic gates to the complex interactions of the CPU, memory, and I/O devices. Key topics include instruction set architectures(ISAs), pipelining, memory hierarchies (cache), and parallel processing techniques. Students will explore the trade-offs involved in designing high-performance and energy-efficient computing systems.

3/1

Computer Vision

• - Image representation and classification
• - Object detection, semantic segmentation
• - Metric learning and Image retrieval

• - Object tracking, matching and fitting

Teaches how machines perceive and analyze images and videos. Topics include filtering, edge detection, feature extraction, and object recognition. Students apply deep learning to visual tasks. Projects may involve facial recognition or gesture analysis. It’s vital for robotics, surveillance, and human-computer interaction.

2^nd Semester of 3^rd Year

3/2

Operating Systems

• - Process scheduling
• - Memory management
• - File systems
• - Device management

Focuses on the design and functioning of modern operating systems. Topics include processes, memory management, file systems, concurrency, and scheduling. Students explore synchronization, deadlocks, and security. Labs provide experience with shell commands and low-level programming. The course is essential for system-level programming and robotics control.

3/2

Embedded Systems

• - Microcontroller programming
• Real-time operating systems

• - Hardware interfacing

Explores programming microcontrollers and real-time systems. Students learn about interrupts, timers, and sensor integration. Labs involve building systems that respond to environmental inputs. The course supports robotics, IoT, and smart device development. Emphasis is on efficiency and hardware-software co-design.

3/2

Mobile Programming

• - Layout & Navigation
• - Activity & Fragment
• - UI
• - Permission

Introduces mobile application development for Android or iOS platforms. Students learn to design intuitive user interfaces and implement app functionality using frameworks such as Flutter, React Native, or native SDKs. Topics include mobile architecture, event handling, data storage, and device integration. Labs focus on building real-world apps that utilize GPS, sensors, or cloud APIs. Prepares students for careers in mobile software and cross-platform development.

3/2

Software Engineering

• - Software design models
• - Testing and maintenance
• - Agile methodologies

Teaches principles of software development lifecycle, from requirements to maintenance. Students use UML diagrams, agile methods, and version control tools. Emphasis is placed on teamwork, documentation, and testing. Students build medium-sized projects using iterative development. The course prepares students for collaborative software work in industry.

3/2

★ Deep Learning

• - CNN
• - RNN, LSTM,
• - Generative model
• - Unsupervised learning

• - Reinforcement learning

Focuses on the design and implementation of deep neural networks. Topics include backpropagation, convolutional networks (CNNs), recurrent neural networks (RNNs), and generative models. Students use frameworks such as TensorFlow or PyTorch to build deep learning applications. Emphasis is placed on handling large datasets, model tuning, and overfitting prevention. Projects include tasks like image classification, language generation, or recommendation systems.

3/2

Robot Operating System (ROS)

• - Message Passing
• - Sensor Integration
• - Gazebo Simulation
• - Modular Robotics Software

Introduces ROS as a framework for developing robotic software. Students learn message passing, sensor drivers, and robot control. Labs include working with Gazebo simulations or physical robots. Emphasis is on modular, scalable, and real-time robotic systems. ROS is an industry-standard in robotics development.

1^st Semester of 4^th Year

4/1

Natural Language Processing

• - Tokenization
• - Embedding
• - RNN, Transformer
• - Large Language models

Focuses on machine understanding and generation of human language. Students learn text preprocessing, language modeling, parsing, and sentiment analysis. Applications include chatbots, translation, and voice assistants. Projects involve building NLP pipelines using NLTK or spaCy. It connects linguistics with AI technology.

4/1

Big Data

• - Large-Scale Data Processing
• - Data Modeling and Optimization Problems

• - Large-Scale Learning

Explores the immense challenges and opportunities presented by datasets too large and complex for traditional processing methods. You'll gain expertise in Large-Scale Data Processing, mastering techniques and tools for handling massive volumes of information. We will delve into Data Modeling and Optimization Problems, focusing on efficient data representation and query performance. Furthermore, the course covers Large-Scale Learning, equipping you with the ability to build and deploy machine learning models on vast datasets.

4/1

Computer Networks

• - OSI
• - TCP/IP models
• - Routing

• - Switching

Explores internet architecture and networking protocols such as TCP/IP, HTTP, and DNS. Students learn socket programming, routing, and network security. Labs involve building client-server applications. The course emphasizes reliability, scalability, and data transmission. It supports IoT and robot communication.

4/1

Compiler

• - Lexical analysis
• - Parsing
• - Code generation
• - Optimization

Unravels the fascinating process of transforming high-level programming languages into executable machine code. You will gain a deep understanding of the core phases involved, starting with lexical analysis, which breaks down source code into tokens. Next, parsing constructs a syntactic structure, ensuring the code adheres to language rules. Students then move to code generation, where the intermediate representation is translated into machine-understandable instructions. Finally, optimization techniques are explored to enhance the efficiency and performance of the generated code.

4/1

★ Capstone Project I - Design

• - Problem Definition
• - System Design
• - Project Planning
• - Technical Proposal

Students begin a year-long team project focused on AI or robotics. They identify a real-world problem and define requirements and design specifications. The course emphasizes project planning, research, and proposal writing. Students present progress in peer reviews and faculty mentoring sessions. It fosters teamwork, innovation, and communication.

4/1

Intelligent Robotics

• - Autonomous Decision-Making
• - Sensor Fusion
• - SLAM (Simultaneous Localization and Mapping)

Explores autonomy in vehicles, drones, and mobile robots. Topics include SLAM, localization, motion planning, and control architectures. Students implement algorithms that allow robots to make decisions. Labs focus on navigation in dynamic environments. The course prepares students for research or careers in autonomy.

2^nd Semester of 4^th Year

4/2

Advanced Deep Learning

• - Transformers & Attention Mechanisms
• - Generative Adversarial Networks (GANs)
• - Graph Neural Networks (GNNs)
• - Model Scalability & Optimization

This course explores state-of-the-art deep learning architectures and techniques beyond introductory neural networks. Topics include attention mechanisms, Transformers, generative adversarial networks (GANs), graph neural networks (GNNs), and self-supervised learning. Students engage with advanced model tuning, scalability, and interpretability challenges. Hands-on projects involve building cutting-edge models for tasks such as image generation, language modeling, or video analysis. The course prepares students for research, innovation, or graduate study in artificial intelligence.

4/2

Information Security

• - Cryptography
• - Risk assessment

• - Ethical hacking

Explores the principles and practices of securing computer systems and networks. Topics include encryption, authentication, access control, intrusion detection, and vulnerability analysis. Students learn about common threats and how to defend against them using cryptographic techniques and secure protocols. Labs include hands-on security testing and ethical hacking exercises. The course emphasizes cybersecurity awareness and system resilience.

4/2

★ Capstone Project II -Implementation & Presentation

• - System Integration
• - Testing & Evaluation
• - Final Presentation & Demonstration
• - Technical Documentation

Continuation of Capstone I with full system implementation. Students develop, test, and refine their intelligent system or robotic solution. Deliverables include a final report, public presentation, and demonstration. The course emphasizes engineering rigor and creativity. It culminates the undergraduate journey.