For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| AIM5026 | Introduction to Robotic Intelligence | 3 | 6 | Major | Master/Doctor | - | 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. | |||||||||
| AIM5027 | Advanced AI-Robot Computing | 3 | 6 | Major | Master/Doctor | - | No | ||
| ThiscourseteachesbasiccomputerprogramminglanguageandOSenvironmenttoimplementAIalgorithmandRobotControl.ItlearnsLinuxandadvancedc++andPythonprogramminglanguage.OpenCV,OpenGL,Boost,whicharewidelyusedforAIandVision,andNumpy,Matplotlib,andPillowwhicharewidelyusedforlearningalgorithms.AftertheProject,weunderstandthebasicprinciplesofdesigningsuchaprocedurebyunderstandingtheoperatingprinciplesoflearningalgorithmsappliedinvariousfieldsanddefiningnecessaryrequirements. | |||||||||
| AIM5028 | SW-HW Integrated Design | 3 | 6 | Major | Master/Doctor | - | No | ||
| SW-HW Integrated Design Methodology covers SW and HW integrated desgin methods to design the efficient Artificial Intelligence (AI) system for various applications. Optimum partitioning between SW and HW is needed considering the data processing speed, power consumption, and complexity and optimum performance can be achieved. This course covers AI SW design methodology, AI HW design methodology, and AI SW-HW design methodology. | |||||||||
| AIM5029 | AI Colloquium | 1 | 2 | Major | Master/Doctor | - | No | ||
| Thisclassprovidesbroadknowledgeaboutmanyfieldsofinformationtechnology.VarioussubjectsareselectedwhicharecurrentlyhotissuesinArtificial Intelligence andinvitedtalksaregivenabouttheselectedsubjects. | |||||||||
| AIM5030 | Intellectual Property Right and AI Ethics | 2 | 4 | Major | Master/Doctor | - | No | ||
| Thiscourseis mainly composed of two topics. Understanding of intellectual property rights and AI ethics. First topic providesacomprehensiveintroductiontointellectualproperty(IP)suchascopyright,patents,trademarks,andlicensingetc.Itincludesthefollowings:defineintellectualpropertyrights;identifyandunderstandthevariouslawsgoverningintellectualproperty;understandandanalyzetheimpactoftechnologyonintellectualpropertylaws;understandthesocial,cultural,economicimpactofintellectualpropertylaws;recognize,understand,andanalyzetheimpactofintellectualpropertylawsontheproduction,management,organization,anddisseminationofinformationandknowledge;wheretofindandhowtouseintellectualinformation.AI ethics will study various ethical principles such as fairness, transparency, safety, stability and responsibility to prevent unforeseen tragedies that can occur when AI is used. | |||||||||
| AIM5031 | AI Master Independent Research | 3 | 6 | Major | Master | Korean | Yes | ||
| Thisisagraduate-levelcourseforstudentspursuingamaster degree.Inthiscourse,studentsidentifyaresearchproblemandperformindependentresearchontheselectedproblemduringthesemesterundertheguidanceoftheiradvisors. | |||||||||
| AIM5032 | AI PhD Independent Research | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| Thisisagraduate-levelcourseforstudentspursuingadoctoraldegree.Inthiscourse,studentsidentifyaresearchproblemandperformindependentresearchontheselectedproblemduringthesemesterundertheguidanceoftheiradvisors. | |||||||||
| AIM5033 | Multi-modal Learning | 3 | 6 | Major | Master/Doctor | - | No | ||
| Real-word data usually contain different types of modalities. For example, images can be associated with short descriptions and in a document, some ideas are illustrated by an image even though most of the ideas are delivered by texts. This graduate course will discuss multi-modal learning techniques which integrate different types of data in a machine learning method. | |||||||||
| AIM5035 | Explainable AI | 3 | 6 | Major | Master/Doctor | - | No | ||
| Recently, deep neural networks are demonstrating superb prediction accuracy, but their complex architectures make it hard to explain its prediction results. In this course, we aim to cover the recent efforts on interpreting the complex decision process of deep neural networks. First, we cover the visualizations of the learned representations of neural networks, then consider the saliency-map based interpretation methods (e.g., Grad-CAM and LRP). We will also cover black-box interpretation methods (e.g., LIME, SHAP) and look at the robustness of such methds. Moreover, we will look at important applications that requires interpretability and carry out term-projects. | |||||||||
| AIM5036 | Deep Generative Model | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| We cover the topics on deep generative models, which are attracting lots of attention. First part include autoregressive models, variational autoencoder (VAE), normalized flow, and the second half will be dedicated to generative adversarial networks (GAN) and its variations such as Wasserstein GAN, conditional GAN, and Cycle GAN, et. We will also cover various application areas that utilize generative models. | |||||||||
| AIM5038 | Context-aware Learning | 3 | 6 | Major | Master/Doctor | - | No | ||
| Context-awareness refers to the idea that a computer (or any programmable device) can sense and react based on their environment. The emphasis is on making a machine appropriately react to the dynamic environment that the machine faces. This graduate course will discuss context-aware learning methods that allow intelligent devices to be aware of their contexts and react based on appropriate decision making. | |||||||||
| AIM5039 | Intelligent Storytelling | 3 | 6 | Major | Master/Doctor | - | No | ||
| In this course students will learn theories on interactive storytelling, computational models of interactive narrative based on AI technology, and practice/demonstration/exercise on interactive storytelling systems and authoring tools. After taking the class, students should be able to apply theoretical narrative models, AI techniques, and authoring tools for building interactive narrative systems. In addition, they are expected to propose and evaluate research ideas in interactive storytelling systems | |||||||||
| AIM5040 | Unsupervised learning | 3 | 6 | Major | Master/Doctor | - | No | ||
| We cover the basic and advanced topics on unsupervised learning, which directy learns from unlabeled data. The basic topics will cover K-means clustering, principal component analysis (PCA), independent component analysis (ICA), and expectation-maximization (EM), hidden Markov models (HMM). The more advanced topics include restricted Boltzman machine (RBM) and deep Boltzmann machine (DBM). | |||||||||
| AIM5041 | Affective Computing | 3 | 6 | Major | Master/Doctor | - | No | ||
| The primary goal of the course is to understand computational models of emotions. The course investigates AI-based technologies and algorithms. The learning contents include definition of emotion, emotion recognition using machine learning algorithms, cognition-based emotion processing model, and multimodal emotion expressions. After taking the course, students are expected to apply the models in different applications. | |||||||||
| AIM5042 | Game AI | 3 | 6 | Major | Master/Doctor | - | No | ||
| Artificial intelligence is one of the essential components of a computer game, and computer games can be referred as testbeds for human-level intelligence of computers. This lecture introduces various game AI techniques including state machines, decision making algorithms for both realism and entertaining experiences, path finding, and strategy making. | |||||||||