Course Name

Advanced Data Mining

Summary

Lecture Objectives

• ● This lecture introduces the core techniques in machine learning and data mining, which are important technologies for both data analysis and decision making and supports.● The main contents includes data warehouse, association rules and correlation analysis, classification and prediction, clustering, dimension reduction, Bayesian approach, and so on.

Course Name

Advanced Database System

Summary

Lecture Objectives

• ● Based on the theories learned in the basic database course, R&D theories, case studies, and trends on database systems and applications that have recently become issues in the field of big data are studied.● In particular, we learn how to use databases that are widely used in science and technology, such as biological databases like GenBank and NDB, and learn the ability to develop applied research/services based on them.

Course Name

Advanced Big Data

Summary

Lecture Objectives

• ● This lecture deals with techniques of acquisition, pre-processing, analysis, and visualization for structured or unstructured big data, and studies practical skills through application practice using big data in various fields.● In addition, we learn distributed/parallel processing and streaming data processing technologies for continuously inflowing data, and its applications to big data analysis.

Course Name

Advanced Deep Learning with Research Data

Summary

Lecture Objectives

• ● Students acquire the ability to use deep learning technology based on various data such as text, images, and videos used in the field of science and technology.● In particular, we learn the ability to develop deep learning applications by using various deep learning techniques like RNN, transformer, CNN, GAN, and deep reinforcement learning.

Course Name

Applied AI 1: Research Data Analysis

Summary

Lecture Objectives

• ● This lecture introduces basic technologies for effective processing and analysis of big text data. The main contents are AI-based natural language processing technologies such as pre-processing, part-of-speech analysis, frequency analysis, text classification, RNN, and transformer.● Also, we learn how to process Korean text.

Course Name

Applied AI 2: Multimedia Data Analysis

Summary

Lecture Objectives

• ● This lecture introduces multimedia data processing techniques based on the latest AI methods.● Main contents include multimedia (image/voice/video) data processing, CNN theory and the latest CNN model, image classification, image search, object detection, semantic segmentation, image captioning, image generation (GAN, style transfer, etc.), and data augmentatioon, transfer learning, multi-modal learning, etc.

Course Name

Applied AI 3: Solution of Social Issues

Summary

Lecture Objectives

• ● This lecture introduces the latest artificial intelligence technologies focused on KISTI research sites.● Main contents include the introduction of the latest artificial intelligence technologies specialized for analyzing and solving social problems or national disaster cases (eg, infectious diseases, carbon reduction, etc.).

Course Name

Advanced Artificial Intelligence

Summary

Lecture Objectives

• ● This lecture introcudes computational mathematics for understanding AI/ML, decision tree, classification, Bayesian, SVM, PCA, matrix factorization, random forest, ensemble, and other core techniques of AI/ML are introduced.● In particular, we study theories, case studies, and trends in AI/ML R&D based on national science and technology information and research data, and intend to acquire the ability to develop applied AI research/services based on the science and technology data used in practice.