Inspiring Future, Grand Challenge

Search
Close
search
 

Academic Programs

  • home
  • Academic Programs
  • Graduate
  • Department of Semiconductor Display Engineering
  • Course&Curriculum

Department of Semiconductor Display Engineering

For more details on the courses, please refer to the Course Catalog

교육과정
Code Course Title Credit Learning Time Division Degree Grade Note Language Availability
AIM4001 Advanced Big Data Analytics 3 6 Major Bachelor/Master Artificial Intelligence - No
This course introduces fundamental data mining and machine learning techniques for big data analytics. The emphasis in the course will be learning key techniques that are required to extract meaningful information from big data, and developing scalable data mining algorithms for big data analytics. The first half of the course will cover various supervised and unsupervised machine learning methods (theoretical analysis of the methods and their practical applications), and the last half of the course will focus on scalable graph mining techniques with special emphasis on analyzing large-scale social networks. There will be one midterm, three assignments, and the final project where students will be expected to develop scalable algorithms for collecting and analyzing big data.
AIM4001 Advanced Big Data Analytics 3 6 Major Bachelor/Master Artificial Intelligence - No
This course introduces fundamental data mining and machine learning techniques for big data analytics. The emphasis in the course will be learning key techniques that are required to extract meaningful information from big data, and developing scalable data mining algorithms for big data analytics. The first half of the course will cover various supervised and unsupervised machine learning methods (theoretical analysis of the methods and their practical applications), and the last half of the course will focus on scalable graph mining techniques with special emphasis on analyzing large-scale social networks. There will be one midterm, three assignments, and the final project where students will be expected to develop scalable algorithms for collecting and analyzing big data.
AIM5001 Theories of Artificial Intelligence 3 6 Major Master/Doctor Artificial Intelligence Korean Yes
In this course students will learn the fundamental algorithms of Aritificial Intelligence including the problem solving techniques, search algorithms, logical agents, knowledge representation, inference, and planning. After taking the course, students are expected to implement the algorithms using computer programming languages.
AIM5004 Deep Neural Networks 3 6 Major Master/Doctor Artificial Intelligence - No
In this class, we will cover the following state-of-the-art deep learning techniques such as linear classification, feedforward deep neural networks (DNNs), various regularization and optimization for DNNs, convolutional neural networks (CNNs), recurrent neural networks (RNN), attention mechanism, generative deep models (VAE, GAN), visualization and explanation.
AIM5025 Intelligent Robot and System 3 6 Major Master/Doctor 1-4 Artificial Intelligence - No
Inordertouserobotsveryefficiently,robotsarerequestedtobeabletoperformalltasksashumanscan.Thiscoursediscussesthetechniqueofsensoranditsapplicationinordertomakerobotsperformtasksintelligently.
AIM5026 Introduction to Robotic Intelligence 3 6 Major Master/Doctor Artificial Intelligence - No
Robot is defined as an intelligent system connecting sensors and actuators. As an intelligent system, robot is to play a key role for providing necessary services to human by automatically carrying out tasks requiring navigation and manipulation. To this end, robot needs to recognize objects and understand surroundings while reasoning and planning the behaviors necessary for carrying out tasks. Especially, it is essential for robot to be able to obtain its capabilities of recognition and understanding of environments as well as of reasoning and planning of behaviors by learning. This course deals with the fundamentals of robot intelligence on how robot learns for the recognition and understanding of environments as well as for the reasoning and planning of behaviors associated with manipulation and navigation.
AIM5064 Special topics in visual computing 3 6 Major Master/Doctor 1-4 Artificial Intelligence Korean Yes
This is a graduate seminar course in visual computing. We will survey and discuss the recent research papers in computer vision area, such as image recogniaion, reconstruction, 3D vision, simulation, generative models, etc. Throughout this course, students get familiar with the recent innovations in computer vision area and identify open questions and new research directions in this field.
AIM5065 OPEN AI NETWORKING 3 6 Major Master/Doctor 1-4 Artificial Intelligence English Yes
Mobile/wireless networks are going through a new AI revolution triggered by the challenges of hyper-connectivity, hyper-low latency communication, and massive data orchestration for enormous connected objects. As such, they are one of the most active research areas in Beyond 5G and 6G in terms of growth and innovation. The “AI and 5G/6G” course covers basic knowledge of 5G/6G mobile networks and available AI technologies for improved network performance and efficient management of resources. In particular, the course is split in three parts, where the first part discusses basic 5G architecture and new technologies that are shaping 6G architecture, such as cloud-native computing, AI-native communication, and deterministic networking. Second part covers the state-of-the-art Deep Learning (DL) approaches that are relevant for 5G/6G mobile networks, like recurrent models, generative adversarial networks, transformer networks, and deep reinforcement learning. Third part presents the latest case studies of AI based dynamic orchestration of network behavior by using parameters like traffic variation, localization, mobility, and user context. At the end of the course, the student will have a comprehensive vision of 5G/6G mobile networks and relevant state-of-the-art AI technologies that open up numerous industrial, management, and research opportunities.
CHS7001 Introduction to Blockchain 3 6 Major Bachelor/Master/Doctor Challenge Semester - No
This course deals with the basic concept for the overall understanding of the technology called 'blockchain'. We will discuss the purpose of technology and background where blockchain techology has emerged. This course aims to give you the opportunity to think about the limitations and applicability of the technology yourself. You will understand the pros and cons of the two major cryptocurrencies: Bitcoin and Ethereum. In addition, we will discuss the concepts and limitations about consensus algorithm (POW, POS), the scalability of the blockchain, and cryptoeconomics. You will advance your understanding of blockchain technogy through discussions among students about the direction and applicability of the technology.
CHS7002 Machine Learning and Deep Learning 3 6 Major Bachelor/Master/Doctor Challenge Semester - No
This course covers the basic machine learning algorithms and practices. The algorithms in the lectures include linear classification, linear regression, decision trees, support vector machines, multilayer perceptrons, and convolutional neural networks, and related python pratices are also provided. It is expected for students to have basic knowledge on calculus, linear algebra, probability and statistics, and python literacy.
CHS7003 Artificial Intelligence Application 3 6 Major Bachelor/Master/Doctor Challenge Semester - No
Cs231n, an open course at Stanford University, is one of the most popular open courses on image recognition and deep learning. This class uses the MOOC content which is cs231n of Stanford University with a flipped class way.  This class requires basic undergraduate knowledge of mathematics (linear algebra, calculus, probability/statistics) and basic Python-based coding skills. The specific progress and activities of the class are as follows. 1) Listening to On-line Lectures (led by learners) 2) On-line lecture (English) Organize individual notes about what you listen to 3) On-line lecture (English) QnA discussion about what was listened to (learned by the learner) 4) QnA-based Instructor-led Off-line Lecture (Korean) Lecturer 5) Team Supplementary Presentation (Learner-led)   For each topic, learn using the above mentioned steps from 1) to 5). The grades are absolute based on each activity, assignment, midterm exam and final project.   Class contents are as follows. - Introduction Image Classification Loss Function & Optimization (Assignment # 1) - Introduction to Neural Networks - Convolutional Neural Networks (Assignment # 2) - Training Neural Networks - Deep Learning Hardware and Software - CNN Architectures-Recurrent Neural Networks (Assignment # 3) - Detection and Segmentation - Generative Models - Visualizing and Understanding - Deep Reinforcement Learning - Final Project.   This class will cover the deep learning method related to image recognitio
COV7001 Academic Writing and Research Ethics 1 1 2 Major Master/Doctor SKKU Institute for Convergence Korean Yes
1) Learn the basic structure of academic paper writing, and obtain the ability to compose academic paper writing. 2) Learn the skills to express scientific data in English and to be able to sumit research paper in the international journals. 3) Learn research ethics in conducting science and writing academic papers.
COV7001 Academic Writing and Research Ethics 1 1 2 Major Master/Doctor SKKU Institute for Convergence Korean Yes
1) Learn the basic structure of academic paper writing, and obtain the ability to compose academic paper writing. 2) Learn the skills to express scientific data in English and to be able to sumit research paper in the international journals. 3) Learn research ethics in conducting science and writing academic papers.
DMC5005 Writing of IT Technical Papers in English 2 4 Major Master/Doctor 1-4 Digital Media Communication - No
This course applies to technical writing of papers and documents in English for students in electronic, electrical, and computer engineering. It is utmost important to be able to write technical papers and documents precisely and tersely in English according to a certain rule and formality, and it is a crucial part of doing research. Through learning technical writing in English and performing such writings, students can improve their written communication skill with other people for technical matters.
DMC5006 Humanities and Technology 1 2 Major Master/Doctor Digital Media Communication - No
Specialized humanities course for students majoring in electronic, electrical, and computer engineering. It is a humanistic journey for developing human-oriented technology that let students feel from the head to the chest and from the chest to the toe rather than studying the liberal arts. Through human-oriented thinking and case study, it targets for broadening the horizons beyond the technology-oriented problem solving and for recognizing that the ultimate goal of product development and technology development is for human. The lectures are given by professors in College of Liberal Arts, School of Business, and College of Information and Communication Engineering.