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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The goal of this course is to expose undergraduate students to recent research problems and results in the selected area of research.

Classical artificial intelligence algorithms and introduction to machine learning based on probability and statistics.

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.

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.

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.

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.

This course will cover advanced and state-of-the-art machine learning such as graphical models, Bayesian inference, and nonparametric models.

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.

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.

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.