Advanced Artificial Intelligence (3)

The objectives of this course are (1) to instruct algorithms and methods of knowledge representation, knowledge learning and rule extraction for developing an intelligent system, (2) to enable students to apply these techniques in application which are related to their own interests, and (3) to improve the students' ability to solve problems.

 

Networks (3)

Students learn about the latest 5G technologies and services, and present and discuss 6G prospects and technologies.

 

Topics in Bioinformatics (3)

Bioinformatics is a rapidly growing interdisciplinary area encapsulating the analysis of information present in biological systems as well as the use of bio-molecules in the construction of future information processing systems. This course focuses on a selection of computational problems of molecular biology and genomics such as: sequence alignments, hidden Markov models, multiple alignment algorithms, andDNA-seq, ChIP-seq, ATAC-seq, and RNA-seq data analysis.

 

Software Quality Management (3)

This course covers various topics for developing quality software. They include Software Quality Overview, Software Inspection, Static Analysis and Software Testing.

 

Security for Internet of Things (3)

In this course, we will study about two principal issues in Internet of Things such as Security/Privacy issues and Intelligence technology. In Security/Privacy issues, we will study the security issues in IoT communication protocols (CoAP, MQTT), Mashup security issues (OpenAPI securities such as OAuth, OpenID), Platform security issues (authentication/authorization, key management). Also, we will study about the intelligence technology from the probabilistic machine learning perspective such as basic knowledge for Deep Learning.

 

Stream Big Data Processing (3)

C++ data structures, C++ file structures, continuous query, R-tree, B-tree, complex event processing.

 

Natural Language Processing (3)

This course gives an overview of main thrusts in natural language processing (NLP) to students, starting with fundamental concepts and ideas, leading into more recent directions of statistics-based probabilistic approaches in this area. The strengths and weaknesses of established systems used in machine translation, informationretrieval, user interface and knowledge extraction will be analyzed in this course, and then the future directions will be provided as well. This course helps students to understand not only the algorithms available for processing linguistic information but also the underlying computational properties of natural languages. Morphological, syntactic, and semantic processing from both linguistic and algorithmic perspectives are considered.

       

Functional Programming (3)

This course is for studying programming techniques in functional languages. Since the functional language has generally simple semantics than imperative languages, it support a special paradigm for programming. In order to study programming in functional languages, we willl use Haskell. Students shall have chances to practice programming techniques in general with several programming assignments in functional languages.

 

Topics in Software Engineering (3)

This course aims to stuyding more adavanced and various topics in software engineering fields. In especial, this course focuses on software design including architectural design, detailed design, and patterns.

 

Advanced Operating Systems (3)

Improving the ability to develop softwares on Linux system based on the understanding Linux interface and tools. Understanding the internal principles of Unix/Linux.

 

Spatial Awareness (3)

In this course, we aim to learn the geospatial standards, which serve as a basis for desgining and implementing spatial information and spatial awareness.

 

Advanced Robot Vision (3)

Terminolgy and concepts of image processing, pattern recognition, and computer vision. Basic theories of filter, mask, and so on. Advanced theories of image enhancement, edge detection, and target tracking for robot vision.

 

Deep Learning (3)

This course aims to understand the fundamentals of the deep learning architectures and to apply deep learning approaches for individual projects

 

Intelligent System for IoT (3)

In this course we will study about the IoT platform, Blockchain platform and their security Issues.

Also, students will be learned about the internal algorithms and mechanisms in blockchain.

 

Topics in Blockchain (3)

In this course, students will learn how to write various application programs using blockchain technology in real life by learning the fundamentals of blockchain, application knowledge, platform, and how to use them. Specifically, students will learn the operation principle of various functions that constitue common, data, agreement, execution and application layers of blockchain. Also, students will conduct an exercise to understand how the functions that construct each layer through smart contract generation are actually implemented in a blockchain platform.

 

Advanced Computer Architectures (3)

Study the concept of advanced high-performance computer systems. Understand and evaluate design trade-offs.

 

Advanced Image Processing (3)

This course is to study color image processing techniques and advanced image processing techniques developed recently. Fundamental image processing techniques for grey scale images are also reviewed. Color vision in human eyes and color science are first introduced and then color image processing techniques are studied in detail. Morphological operation and wavelet transformation are also dealt throughly.

 

Big Data Processing Platform (3)

In this course we will investigate current topics in cloud computing. From the literature we will become familiar with the most advanced approaches being applied in the field. We will analyze each new approach and identify the role of current technology, such as security and databases in cloud systems. We will perform several exercises using a private cloud and a public cloud.

 

Evolutionary Algorithms (3)

Introduces basic principle of evolutionary computation and studies how evolutionary algorithms can be applied to general search, optimization, and machine learning. Other subjects to be discussed include coevolution, estimation of distribution algorithm, constrained optimization, multiobjective optimization, swarm intelligence, artificial immune systems, genetic programming. Term project: design and implementation of an evolutionary algorithm to solve a problem of interest.

 

Mining massive data sets (3)

Understand novel advanced skills in mining of massive datasets and apply these skills for real massive data sets in biomedicine

 

Machine Learning for Visual Computing (3)

The technology that processes video information on a computer is simply a computer vision. The technology of processing image information in a device including a computer is called machine vision. This technology has a variety of applications such as image search, image understanding, apps, mapping, medicine, drones, autonomous vehicles. The core of this application starts with the recognition of image information. In recent years, it has shown excellent efficiency in access recognition systems using neural networks (so-called deep running). This course is focused on understanding fundamental algorithms and cutting-edge research in machine vision.

 

Special issues in Smart City Security (3)

In this course, we will study the advanced topics for Smart City Security. Specifically, we will study about the basics on Differential Privacy, AI techniques for security, Homomorphic Encryption techniques, Anonymous Credential issues for Smart City applications, Zero-knowledge Proof Protocol for Platforms, ECC, isogeny, PQC, etc.

 

Advanced Neural Networks (3)

Introduce basic theories of neural networks. How to apply neural networks to real-world applications.

 

Development Methodology (3)

This course covers: object-oriented requirement modeling, object oriented analysis & design. The course will provide team-based project. Each student will join a team. Each team choose a proper topic as a target system for applying the discussed OO techniques.Each team will perform OO modeling using a CASE Tool and present and revise the artifacts based on comments.

 

Intrusion Detection Algorithm (3)

Network intrusion detection is an important and dynamic study area. Many network intrusion detection methods and systems have been proposed in the literature. In this course, students briefly understand a large number of network intrusion detection methods and systems. In addition, we also discuss tools that can be used by network defenders and datasets that researchers in network intrusion detection can use.

 

IoT Networking and Computing (3)

Study various IoT networks protocols on the path from IoT device, via IoT gateway & IoT platform to application layer. Study SDN & network virtualization concept. Special issues in CPS control and Analytics Through this course, we will study about the digital twin technology, which is important issues in CPS(Cyber Physical System). Digital twin emulates the state and operation of physical equipment and services through modeled simulator. The digital twin simulator understands previous and current status and it predicts future states of modeled target.In this course, we will study about the digital twin concept, and how we implement the simulator and the intelligent engine for digital twin.

 

Big Data Analysis (3)

The course will discuss data mining algorithms for analyzing very large amounts of data. The emphasis will be on MapReduce as a tool for creating parallel algorithms that can process very large amounts of data.

 

Software Reuse Methodology (3)

The objectives of this course are to define what is meant by software reuse, to discuss the knowledge in the most effective way to produce high-quality software systems based on software reuse.

 

Advanced Information Retrieval (3)

This class will introduce classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. By solving given simple examples in reports, students will be able to understand a real Information Retrieval. Students'll be measured as an mid-test and an final test for understaning.

 

Network and System Security (3)

Through this course, we will study about the latest topics on network and system security. Most security technologies are based on a security anchor called HSM (Hardware Security Module) to provide system security. HSM enables safe key management and encryption technology. Particularly, in this course, PUF (Physically Unclonable Function) is studied. PUF is a competent technique because it provides secure key storage, device authentication and attestation. Since the PUF is made as a Physical circuit such as FPGA or ASIC, we need to learn about the basics on hardware security. Also, besides HSM technology, anomaly detection technique which is based on machine learning is also studied. We also study deep learning technique, special area of ML.

 

Big Data Storage and Management (3)

This course addresses fundamental concepts of big data storeage and management system. Emphasis is put on novel approaches/paradigms to managing Big Data. The course aims at a mixture of system issues and hands on experience (like Hadoop/HDFS/NoSQL/Graph Databases) and on fundamental algorithms and techniques (such as consistent hashing or Bloom filters).

 

Computational Behavior Recognition (3)

understanding how computations in the brain and computers enable rapid and efficient object perception. drawing on recent research in Psychology, Neuroscience, Computer Science, and Machine learning. surveying recent findings in multiple disciplines to examine how computations, representations, and their. implementation in the visual system support efficient recognition and categorization. exploring the potential utility of hierarchical processing in the visual system and how it might provide a foundation for informational hierarchies

 

Advanced Digital Signal Processing (3)

Introduce the discrete time signals and systems. Understand the frequency characteristics of the time signals. Understand fundamentals and applications of the FIR and IIR filters. Introduce the new trend to modern signal processing techniques.

 

Applied Graph Theory (3)

In this class, we will focus especially on the network flow model and its application.

Preliminary, Graphi sequence, Tree Network Flow anlaysis(1,2,3), Connectivity, Graph Coloring, Random Graph and Social Application.A

 

Spatial Databases (3)

This lecture is composed of the following topics. Basic concepts about spatial theories,

spatial database building procedures and methodologies, application development practices.

 

Signal Processing (3)

To learn wavelet theory and wavelets with appliacations in signal processing.