Education

Curriculum

Freshman

Semester Course Number Course Name Credits Classification
1 CAC1100 Computer programming 3 Major Selection

Lecture description

The purpose of this course is to introduce students to the skills, methods, and thinking necessary for problem solving using computer science. In particular, the role of computing as a tool to solve problems existing in reality will be focused on. Students will learn about two main abstract principles: algorithms and data structures, which are applied to information visualization, simulation, computational techniques for data science, and simple optimization problems. Class content will be implemented and conducted in Python language.

2 CSI2102 Object oriented programming 3 Major Required

Lecture description

Based on structured programming, learn the concept of object-oriented language, learn the actual object-oriented language through this, and write programs for application problems using object-oriented language. ADT, Classes, Inheritance, Polymorphism, Virtual Functions, Control Structures, I/O, etc. are covered.

Sophomore

Semester Course Number Course Name Credits Classification
1 CSI2103 Data structure 3 Major Required

Lecture description

Space and Time Complexity, Asymptotic Notation, Data Abstraction(ADT), Arrays, Stacks, Queues, Linked Lists, Trees(binary trees, heaps, binary search trees), Graphs (DFS, BFS, MST, shortest paths), Sorting, Hashing, Heap structures, Search Structures(AVL, trees, Red-Black trees, B_trees)

1 CSI2101 Discrete structure 3 Major Basics

Lecture description

Sets, Propositional/Predicate Calculus, Induction, Recursion, Permutation과 Combination, Inclusion, Recurrence Relation, Graphs, Random Number Generation

1 CSI2101 Probability statistics 3 Major Basics

Lecture description

In order to model an uncertain phenomenon, the characteristics of discrete and continuous random variables are dealt with, and basic statistical techniques, hypothesis verification, and simple regression analysis techniques are covered for model analysis using experimental data.

2 - Introduction to artificial intelligence 3 Major Required

Lecture description

This lecture introduces artificial intelligence from various perspectives, including engineering and scientific viewpoints, starting with the concept of AI definition and model. This course will study the general theories of artificial intelligence models such as expression of basic knowledge about artificial intelligence, problem solving methods, intelligent system development theory, search methods, and learning methods.

2 MAT2011 Linear algebra and its applications - Major Basics

Lecture description

Matrix and system of equations, vector space, linear transformation and matrix representation, structure of vector space, bilinear form and dot product space, determinant, eigenvector and eigenvalue are covered.

2 CSI3108 Algorithm analysis 3 Major Selection

Lecture description

Learn various tools for computer algorithm development (Divide-and-Conquer, Greedy Methods, Dynamic Programming, Backtracking, Branch-and-Bound, Approximation, etc.) and learn how to analyze basic algorithms using these tools.

2 - Probability graph model 3 Major Selection

Lecture description

The probability graph model is a powerful mathematical tool for representing the joint probability distribution of a number of random variables interacting with each other. The probability graph model is widely applied in various fields of artificial intelligence such as media signal processing, natural language processing, and machine learning, and is a model developed based on understanding of probability theory and graph theory. In this lecture, basic probability graph model representations are covered, and their theoretical properties, practical applications, and related algorithms are covered.

Junior

Semester Course Number Course Name Credits Classification
1 - Machine learning 3 Major Required

Lecture description

This course lectures on machine learning, the core technology of artificial intelligence. Starting with basic mathematical knowledge of linear algebra and probability theory, lectures will be given on unsupervised, transductive, and graphical inference along with the core contents of regression and classification. In the second half, the basics of deep learning and the latest contents are lectured.

1 - Distributed Learning System 3 Major Selection

Lecture description

Learn about the basic data management framework used for learning and the framework for machine learning and data analysis algorithm execution. Along with big data platforms, the recent trend is evolving towards cloud and machine learning platforms. Acquire basic knowledge related to this and practice real projects.

1 - Big data analysis and modeling 3 Major Selection

Lecture description

This lecture takes an in-depth look at big data analysis, which has recently become a hot topic in all areas of society. As the production, collection, and processing processes of data are systematized with the development of digital technology, technology that derives meaningful results using various data scattered around us is becoming very important with the development of big data analysis solutions. In this changing environment, this lecture will be conducted as an opportunity to learn not only the humanities and social science paradigm but also various related issues and technical concepts in order to understand big data analysis methods broadly and systematically.

1 - Multi-mode data processing 3 Major Selection

Lecture description

Multimodal data is difficult to analyze and learn, so the relevant skills are acquired. Derive analysis results by comprehensively processing heterogeneous data such as time series, text, image, and voice, learn the contents of machine learning model training, and perform actual projects.

2 - Text mining 3 Major Selection

Lecture description

In order to analyze text data and extract meaningful patterns or useful knowledge from documents, it is essential to understand natural language processing (NLP) based on linguistics as well as machine learning and statistics. In this lecture, you will learn text analysis and visualization methodology using natural language processing techniques.

2 - Data mining 3 Major Selection

Lecture description

This course deals with the background, techniques, and cases of data mining to extract meaningful patterns or rules from a huge amount of data pouring with the development of computers. Learn representative methods such as association rules, classification, prediction, and clustering, and develop knowledge as a data miner through practice and projects to apply them to bio, mobile, and business data.

2 - Deep learning 3 Major Selection

Lecture description

In this lecture, students learn the theory of the latest machine learning techniques such as deep neural networks, convolutional neural networks, recurrent neural networks, generative adversarial learning, and deep reinforcement learning, and artificial intelligence through practice using deep learning tools such as Tensorflow/PyTorch. Learn modeling techniques to solve problems.

2 - GPGPU programming 3 Major Selection

Lecture description

In this course, you will learn programming models, parallel architectures, and optimization techniques for programming in GPGPU. It aims to practically experience parallel programming and optimization of various applications such as matrix operation, reduction, and DNN by utilizing Std thread, OpenMP, and Cuda.

Senior

Semester Course Number Course Name Credits Classification
1 - Information retrieval and recommendation system 3 Major Selection

Lecture description

The purpose of this lecture is to explain the basic theory of information retrieval and recommendation system and recent research trends, and to learn new web-based information retrieval technology for the future. Participants of this lecture can learn basic theories about IR model, index, and system performance evaluation from the system developer's point of view rather than from the user's point of view. In-depth study on Neural IR, an information retrieval technology to which deep learning has been applied recently.

1 - Robot artificial intelligence 3 Major Selection

Lecture description

In order to operate a robot, knowledge of many fields such as vision, motion planning, mechanics, control, and sensors must be harmonized together. In this lecture, you will learn about the theory and basic principles that make up the basis of robots, and introduce new technologies introduced with the development of AI, such as reinforcement learning. We will develop applications that can be applied to industry and real life, targeting a simple robot platform.

1 - Computer vision 3 Major Selection

Lecture description

Computer vision is a field dealing with visual perception among artificial intelligence fields, and it is a field that seeks to understand images by extracting useful information from image data. In this course, starting with the principle of the camera, students learn various theoretical foundations of computer vision, such as color vision, establishing the relationship between the 3D world and 2D images, recognizing and detecting objects in images, and visual recognition using deep learning. aim to

1 - Reinforcement learning 3 Major Selection

Lecture description

This lecture deals with reinforcement learning theory and algorithms. Reinforcement learning is one of the core technologies of artificial intelligence. In particular, it is a technology that mimics how to learn from human experience, and it is a technology of great importance from autonomous driving to application to various industries. It aims to acquire the latest theories and applications of reinforcement learning's representative learning paradigm and deep learning-based reinforcement learning.

1 - Artificial Intelligence Comprehensive Design (1) 3 Major Required

Lecture description

Comprehensive artificial intelligence design (1) and artificial intelligence synthesis so that systematic research such as in-depth research, design of new concepts/methods, and analysis of experimental results can be carried out by setting the latest research topics in progress or required in industry or academia Design (2) is linked and operated as a required major subject.

2 - HCI&AI 3 Major Selection

Lecture description

This course learns programming to implement the definition of HCI field, overall theory, and various elements. After learning about the iterative design used in HCI, the outline of input/output technology, and the design and evaluation of interaction technology, the goal is to learn the methodology of convergence with artificial intelligence in the future HCI field.

2 - Prediction and decision-making system 3 Major Selection

Lecture description

In this lecture, you will learn how to make optimal decisions based on prediction. Prediction is a fundamental technology in artificial intelligence. However, rather than a prediction alone, a prediction has value only when a decision is made and made based on it. It aims to learn techniques such as optimization and game theory to make decisions based on predictive models based on machine learning or deep learning.

2 - Natural language processing 3 Major Selection

Lecture description

Natural language processing (or natural language processing) is an understanding of natural language that mechanically analyzes human speech phenomena into a form that can be understood by a computer, or the general process of expressing such a form in a language that can be understood by humans. means technology. In this lecture, technology used in natural language processing fields such as machine translation and chatbots based on machine learning will be introduced.

2 - AI Security 3 Major Selection

Lecture description

AI is in the spotlight because it shows high performance in many fields, but it raises new security issues that are different from the existing ones. This lecture learns about the security issues inherent in AI technology and techniques to mitigate them. It aims to learn theories used in adversarial attacks and membership inference attacks and the latest techniques to defend them.

2 - Data Models and Visualizations 3 Major Selection

Lecture description

In this lecture, you will learn various techniques for visualizing data by making a model that explains it well from given data. By creating a theoretical model of the data, we can better understand the data and, if necessary, synthesize similar data from the model. Visualization is a basic tool for exploring data, and depending on the visualization method, certain properties of data are well shown. These contents are the basis for future artificial intelligence learning, and introduce theory and practical application technology.

2 - AI Ethics 3 Major Selection

Lecture description

Artificial intelligence is not just a new technology, but a core technology of the 4th industrial revolution that accompanies widespread changes in the social structure. It is necessary to understand not only artificial intelligence technology but also related ethics in order to prevent abuse and ensure the moral and ethical use of technology as well as development of artificial intelligence. In this lecture, ethics related to AI will be examined through various examples.

2 - Artificial Intelligence Comprehensive Design (2) 3 Major Required

Lecture description

Comprehensive artificial intelligence design (1) and artificial intelligence synthesis so that systematic research such as in-depth research, design of new concepts/methods, and analysis of experimental results can be carried out by setting the latest research topics in progress or required in industry or academia Design (2) is linked and operated as a required major subject.