For more details on the courses, please refer to the Course Catalog
Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
---|---|---|---|---|---|---|---|---|---|
ERC3012 | Patent Idea Search and Application | 3 | 6 | Major | Bachelor | 3-4 | Engineering | - | No |
1.Objectives The purpose of this course is to cultivate technology exploration skills of future engineering students through theory study and practice of prior art research in science and technology field. Through this course, you can develop basic knowledge and expertise about intellectual property rights. 2. Class contents (1) Understanding of intellectual property rights (patent, trademark, design, etc.) - Intellectual Property Overview - Patent registration requirements and application procedure - Exercising intellectual property rights - Interpretation of IP claims (2) Prior art research - Prior art subject (technical literature such as patent, thesis) - Patent utilization method and search strategy in each country - Use of prior art research (review of registration possibility, review of invalid reasons, patent map, etc.) - Use of prior art research database (WIPS, KIPIRIS, etc.) - Keyword search method for prior art research - Prior Art Research Practice (3) Distinguishing the difference between my ideas and technologies - Identify possession technologies and ideas - Research and analysis of technology, industry, and policy trends - Knowing how to use analytics tools (Google, tech trends sites, new business trends sites, government information sites) - Deriving ideas and technology differentiation from prior art - Idea / technology applied product line / commercialization model / direction of research | |||||||||
ERC3013 | Technology Commercialization Capstone Design | 3 | 6 | Major | Bachelor | Engineering | - | No | |
This lecture consists of background theory and practice for patent protection and commercialization(technology transfer..) of technologies or engineering ideas. | |||||||||
GCO2001 | Introduction of Programming | 3 | 6 | Major | Bachelor | - | No | ||
This course is offered for non major students to learn programming concept easily. This course would select an easiest programming language to have students grasp an idea of programming languages in general. A student should be able to draw a flowchart of a logic he or she wants to program. Then, a student a learn syntaxes of the language to convert that flowchart of the logic. The course is mainly used for a bridge course to those who wants to study computer sciences as the secondary major. | |||||||||
GCO2002 | Introduction to Artificial Intelligence | 3 | 6 | Major | Bachelor | - | No | ||
It aims to help non major students for learning the introduction to artificial intelligence easily. You will learn the general contents from mathematical theory such as probability and set theory necessary to understand artificial intelligence to artificial intelligence logic and real life examples. It aims to learn the principles and characteristics of AI systems through various real-life examples, and to look at and learn the solution process. | |||||||||
GCO2003 | Foundations of Convergence Science | 3 | 6 | Major | Bachelor | 1-4 | Korean | Yes | |
The course provides an overview of what students learn from the courses of School of Convergence, Sungkyunkwan University. Everyweek, each professor introduces what they study and teach, and discusses the nature of convergence science (trans-disciplinary knowledge). | |||||||||
GCO2005 | Preparing for coding tests with Python | 3 | 6 | Major | Bachelor | 1-4 | - | No | |
Thecourseprovidesan opportunity to cope with real world coding test questions in job interview settings. This course is not just a preparation for job seeking but deepening the coding skills learned from Python, machine learning, and deep learning classes. | |||||||||
GCO2006 | Guided Reading | 3 | 6 | Major | Bachelor | 1-4 | - | No | |
Thecourseprovidesan opportunity for students to read the books recommended by SKKU and/or faculty (50%) and student herself (50%). Students are required to post reading log in a form of essay or video clip. If possible, the books recommended by faculty should be about technology and society. | |||||||||
GCO2007 | Principles of Convergence | 3 | 6 | Major | Bachelor | 1-4 | - | No | |
In this course, we study the basic principles that resulted in convergence, and the characteristics of convergence products/convergence services that are different from existing products/services. This course will be a truly converged subject that studies convergence from computer technology to humanities and social sciences. | |||||||||
GCO2008 | Evaluation of Artificial Intelligence | 3 | 6 | Major | Bachelor | 1-4 | - | No | |
Artificial intelligence should be evaluated not only from the developer's perspective, but also from the user's perspective, and in this class, we provide greater value to future users by subdividing AI technologies and learning the methodology of how those subdivided functions can be evaluated to users. We want to help create artificial intelligence that can do it. | |||||||||
GCO2009 | Big data analysis | 3 | 6 | Major | Bachelor | 1-4 | - | No | |
In this course, students learn the methodology of analyzing data by various data types, types, and sizes using the programming language R, and learn how to interpret the results. In particular, when the data size is large, we learn the methodology of efficiently processing data analysis in a distributed system and expanding the research to research topics of interest for each individual or team. It aims to experience problem solving optimization method by implementing it in R using machine learning, deep learning, and reinforcement learning when necessary for each problem. | |||||||||
GCO2010 | A first course of probability | 3 | 6 | Major | Bachelor | 1-4 | - | No | |
Combinatoril analysis (Counting, Sample space, Event, Prob., etc.)-Combinatorics and Axioms of Prob. (Basic properties of probability, axioms, associations with sets, etc.) are studied in depth, and important examples aim to develop practical skills implemented in Python. In addition, Conditional Prob. Learn the connection between probability theory and advanced machine learning by deeply learning (Conditional Probability) and Bayes's Formula and expanding to Bayesian Network. Students learn the definition, types, Expectation, and Limit Theorems of random variables, and perform team-specific tasks that are expanded by using them in simple cases of artificial intelligence and machine learning applications. | |||||||||
GCO2011 | Introduction to Machine Learning | 3 | 6 | Major | Bachelor | 1-4 | Korean | Yes | |
Contents: Learning basic machine learning algorithms such as probability-based model, Bayesian network, Genetic Algorithm, SVM, DT and so on. Flipped class: Using 70% of pre-recorded video and 30% of real-time classes (on or off), Watch video lectures, and then proceed with necessary class activities such as Q&A through real-time classes. | |||||||||
GCO2012 | Foundations of Convergence Science | 3 | 6 | Major | Bachelor | 1-4 | Korean | Yes | |
Contents: Learn the structures and some issues of perceptron, MLP, and deep neural networks, and conduct exercises and project-based learning. Flipped class: Using 70% of pre-recorded video and 30% of real-time classes (on or off), Watch video lectures, and then proceed with necessary class activities such as Q&A through real-time classes. | |||||||||
GCO2013 | Ethical Issues in Artificial Intelligence | 3 | 6 | Major | Bachelor | 1-4 | - | No | |
This course offers students with opportunities to reflect on moral and ethical issues related to the emergence of artificial intelligence (A.I.). The main objective of the course is to encourage students to think about the direct and indirect impact of A.I. technologies on the modern society by examining real-world cases from various domains. Through this exercise students will become familiar with critical issues which need to be considered when developing A.I. systems. | |||||||||
GCO2014 | Political Communication and Data Analytics | 3 | 6 | Major | Bachelor | 1-4 | - | No | |
This course aims to advance our understanding of pollical communication processes in the digital age. The development and digitization of communication technologies have changed the political scene where citizens and various political actors now directly interact with each other. These developments necessitate the application of new and advanced data science and artificial intelligence techniques to explain how citizens engage in political communication processes. By offering student with opportunities to collect and analyze real-world political communication data, this course will help students to hone research skills necessary for data scientists and AI developers. |