This course provides students with an understanding of digital systems as building blocks of modern digital computers. This course puts emphasis on providing students with hands-on experience on digital systems. The course includes both lecture and laboratory work on the topics of: boolean algebra, binary system, combinatorial logic, asynchronous sequential circuits, algorithmic state machine, asynchronous sequential circuits, VHDL, CAD tools and FPGAs.
English Lecture
Y
CS270
Creative design of intelligent robots
3:0:3
Spring
Course Name
Creative design of intelligent robots
SubTitle
Course Code
CS270
Course Type
Elective Major
Prerequisite
Lecture:Lab:Credit
3:0:3
Level
Undergraduate
Semester
Spring
Course Description
This course aims to provide an opportunity for sophomores to experience creative system design using Lego mindstorm NXT kit and URBI robot software platform. In lectures, robotic CS is introduced and various examples are demonstrated to bring out students' interests. In lab hours, students build own intelligent robot system creatively. Students are educated to integrate hardware and software designs, and make presentations at the end of semester.
English Lecture
Y
CS372
Natural Language Processing with Python
3:0:3
Spring or Fall
Course Name
Natural Language Processing with Python
SubTitle
Course Code
CS372
Course Type
Elective Major
Prerequisite
Lecture:Lab:Credit
3:0:3
Level
Undergraduate
Semester
Spring or Fall
Course Description
The course offers students a practical introduction to natural language processing with the Python programming language, helping the students to learn by example, write real programs, and grasp the value of being able to test an idea through implementation, with an extensive collection of linguistic algorithms and data structures in robust language processing software.
English Lecture
N
CS376
Machine Learning
3:0:3
Spring & Fall
Course Name
Machine Learning
SubTitle
Course Code
CS376
Course Type
Elective Major
Prerequisite
Lecture:Lab:Credit
3:0:3
Level
Undergraduate
Semester
Spring & Fall
Course Description
Machine learning, a sub-field of computer science, has been popular with the era of intelligent softwares and attracted huge attentions from computer vision, natural language processing, healthcare and finance communities to name a few. In this introductory course, we will cover various basic topics in the area including some recent supervised and unsupervised learning algorithms.
English Lecture
Y
CS423
Probabilistic Programming
3:0:3
Spring
Course Name
Probabilistic Programming
SubTitle
Course Code
CS423
Course Type
Elective Major
Prerequisite
CS376, CS320
Lecture:Lab:Credit
3:0:3
Level
Undergraduate
Semester
Spring
Course Description
The course aims at teaching students techniques from machine learning and programming languages that enable the design and implementation of a programming language for easily writing advanced probabilistic models from machine learning. We will cover a wide range of general-purpose algorithms for probabilistic inference, and discuss how these algorithms can be used to build programming languages and systems for developing models from machine learning. We will also study a mathematical foundation of those languages using tools from measure-theoretic probability theory.
English Lecture
N
CS454
Artificial Intelligence Based Software Engineering
3:0:3
Spring
Course Name
Artificial Intelligence Based Software Engineering
SubTitle
Course Code
CS454
Course Type
Elective Major
Prerequisite
Lecture:Lab:Credit
3:0:3
Level
Undergraduate
Semester
Spring
Course Description
This course aims to introduce the operations and applications of metaheuristic and bio-inspired algorithms, including genetic algorithm, swarm optimization, and artificial immune system. By considering diverse problems ranging from combinatorial ones to performance improvement of complex software system, students are expected to learn how to apply computational intelligence to unseen problems.
English Lecture
N
CS470
Introduction to Artificial Intelligence
3:0:3
Fall
Course Name
Introduction to Artificial Intelligence
SubTitle
Course Code
CS470
Course Type
Elective Major
Prerequisite
Lecture:Lab:Credit
3:0:3
Level
Undergraduate
Semester
Fall
Course Description
This course introduces basic concepts and design techniques of artificial intelligence, and later deals with knowledge representation and inference techniques. Students are to design, implement, and train knowledge-based systems.
English Lecture
Y
CS474
Text Mining
3:0:3
Fall
Course Name
Text Mining
SubTitle
Course Code
CS474
Course Type
Elective Major
Prerequisite
Lecture:Lab:Credit
3:0:3
Level
Undergraduate
Semester
Fall
Course Description
This course will introduce the essential techniques of text mining, understand as the process of deriving high-quality information from unstructured text. The techniques include: the process of analyzing and structuring the input text with natural language processing, deriving patterns with machine learning, and evaluating and interpreting the output. The course will cover some typical text mining tasks such as text categorization, text clustering, document summarization, and relation discovery between entities.
English Lecture
Y
CS475
Machine Learning for Natural Language Processing
3:0:3
Fall
Course Name
Machine Learning for Natural Language Processing
SubTitle
Course Code
CS475
Course Type
Elective Major
Prerequisite
Lecture:Lab:Credit
3:0:3
Level
Undergraduate
Semester
Fall
Course Description
This course will cover important problems and concepts in natural language processing and the
machine learning models used in those problems. Students will learn the theory and practice of ML
methods for NLP, read and conduct research based on latest research publications.
English Lecture
Y
CS484
Introduction to Computer Vision
3:0:3
Fall
Course Name
Introduction to Computer Vision
SubTitle
Course Code
CS484
Course Type
Elective Major
Prerequisite
Lecture:Lab:Credit
3:0:3
Level
Undergraduate
Semester
Fall
Course Description
In this course, students will learn the basic principles and techniques of image processing. Expanding the foundations of image processing, they will learn 3-dimensional image processing from camera images and also techniques for deep learning-based image understanding, combined with artificial intelligence. To this end, the curriculum of this course consists of three parts: (1) the basic principles and understanding of image processing, (2) the basic principles and understanding of 3D image processing, and (3) the basic principles and understanding of image processing using artificial intelligence. Students learn and experience basic principles for computer vision and various image processing applications based on the deep understanding of computer vision.
English Lecture
Y
CS492
Special Topics in Computer Science
3:0:3
Spring or Fall
Course Name
Special Topics in Computer Science
SubTitle
(Linear algebra in combinatorics and algorithms)
Course Code
CS492
Course Type
Elective Major
Prerequisite
Lecture:Lab:Credit
3:0:3
Level
Undergraduate
Semester
Spring or Fall
Course Description
The goal of this course is to expose undergraduate students to recent research problems and results in the selected area of research.
English Lecture
Y
Elective Major(Essential)
Code
Subject
Credit
Term
CS570
Artificial Intelligence and Machine Learning
3:0:3
Spring
Course Name
Artificial Intelligence and Machine Learning
SubTitle
Course Code
CS570
Course Type
Elective Major(Essential)
Prerequisite
Lecture:Lab:Credit
3:0:3
Level
Graduate
Semester
Spring
Course Description
Classical artificial intelligence algorithms and introduction to machine learning based on probability and statistics.
English Lecture
Y
CS572
Intelligent Robotics
3:0:3
Fall
Course Name
Intelligent Robotics
SubTitle
Course Code
CS572
Course Type
Elective Major(Essential)
Prerequisite
Lecture:Lab:Credit
3:0:3
Level
Graduate
Semester
Fall
Course Description
The goal of this course is to provide students with state-of-the-art technologies in intelligent robotics. Major topics include sensing, path planning, and navigation, as well as artificial intelligence and neural networks for robotics.
English Lecture
N
CS574
Natural Language Processing I
3:0:3
Spring or Fall
Course Name
Natural Language Processing I
SubTitle
Course Code
CS574
Course Type
Elective Major(Essential)
Prerequisite
Lecture:Lab:Credit
3:0:3
Level
Graduate
Semester
Spring or Fall
Course Description
As a typical application of symbolic AI machine translation (M.T) addresses the major issues involving computational linguistics, rules base, and more fundamentally knowledge representation and inference. In this regard, the goal of the course is to provide students with first-hand experience with a real AI problem. The topics include application of M.T., basic problems in M.T., and classical approaches to the problems.
English Lecture
N
CS576
Computer Vision
3:0:3
Spring or Fall
Course Name
Computer Vision
SubTitle
Course Code
CS576
Course Type
Elective Major(Essential)
Prerequisite
Lecture:Lab:Credit
3:0:3
Level
Graduate
Semester
Spring or Fall
Course Description
The goal of this course is to provide students with theory and application of computer vision. Major topics include digital image fundamentals, binary vision, gray-level vision, 3-D vision, motion detection and analysis, computer vision system hardware and architecture, CAD-based vision, knowledge-based vision, neural-network-based vision.
English Lecture
N
CS579
Computational Linguistics
3:0:3
Fall
Course Name
Computational Linguistics
SubTitle
Course Code
CS579
Course Type
Elective Major(Essential)
Prerequisite
Lecture:Lab:Credit
3:0:3
Level
Graduate
Semester
Fall
Course Description
This course focuses on universal models for languages, especially English and Korean. For computational study, issues on knowledge representation, generalized explanation on linguistic phenomena are discussed. When these models are applied to natural language processing, properties needed for computational models and their implementation methodologies are studied.
English Lecture
N
Elective Major(Elective)
Code
Subject
Credit
Term
CS671
Advanced Machine Learning
3:0:3
Spring or Fall
Course Name
Advanced Machine Learning
SubTitle
Course Code
CS671
Course Type
Elective Major(Elective)
Prerequisite
Lecture:Lab:Credit
3:0:3
Level
Graduate
Semester
Spring or Fall
Course Description
This course will cover advanced and state-of-the-art machine learning such as graphical models, Bayesian inference, and nonparametric models.
English Lecture
N
CS672
Reinforcement Learning
3:0:3
Spring or Fall
Course Name
Reinforcement Learning
SubTitle
Course Code
CS672
Course Type
Elective Major(Elective)
Prerequisite
Lecture:Lab:Credit
3:0:3
Level
Graduate
Semester
Spring or Fall
Course Description
This course covers reinforcement learning, which is one of the core research areas in machine learning and artificial intelligence. Reinforcement learning has various applications, such as robot navigation/control, intelligent user interfaces, and network routing. Students will be able to understand the fundamental concepts, and capture the recent research trends.
English Lecture
N
CS686
Motion Planning and Applications
3:0:3
Fall
Course Name
Motion Planning and Applications
SubTitle
Course Code
CS686
Course Type
Elective Major(Elective)
Prerequisite
Lecture:Lab:Credit
3:0:3
Level
Graduate
Semester
Fall
Course Description
In this class we will discuss various techniques of motion and path planning for various robots. We go over various classic techniques such as visibility graphs and cell decomposition. In particular, we will study probabilistic techniques that have been used for a wide variety of robots and extensively investigated in recent years.
English Lecture
Y
CS774
Topics in Artificial Intelligence
3:0:3
Spring or Fall
Course Name
Topics in Artificial Intelligence
SubTitle
(Social Media Analytics)
Course Code
CS774
Course Type
Elective Major(Elective)
Prerequisite
Lecture:Lab:Credit
3:0:3
Level
Graduate
Semester
Spring or Fall
Course Description
The goal of this course is to provide students with recent theory of AI and its application. It covers information representation. heuristic search, logic and logic language, robot planning, AI languages, expert system, distributed AI system, uncertainty problem and so on.