본 교과목은 데이터과학과 인공지능의 융합을 위한 입문 강의이다. 데이터 과학과 인공지능의 기본개념과 함께, 데이터 분석, 기계학습(최적화, 회귀, 분류, 군집화, 딥러닝, 강화학습), 컴퓨터 비전, 컴퓨터 그래픽스의 기초를 소개한다. 또한 실습 수업을 통해 인공지능의 작동 원리와 활용 방법을 이해한다. 나아가 현업 전문가 특강을 통해 실제 산업 현장에서 인공지능이 어떻게 활용되고 있는지 탐구한다.

본 교과목은 다양한 학문 분야에 적용 가능한 일반적 문제해결 프레임워크로서 시스템 설계 및 최적화 방법을 소개한다.

본 교과목을 통해 학생들은 다음 내용을 학습한다.

• 현실 문제를 최적화 문제로 정식화하는 방법
• 수학적 방법을 활용하여 무제약 최적화 및 제약 최적화 문제를 해결하는 방법
• 반복 최적화, 휴리스틱 최적화 및 메타휴리스틱 최적화 기법을 현실 문제에 적용하는 방법
• 다목적 최적화에서 목적 간 트레이드오프를 정식화하고 분석하는 방법
• 이산사건 시뮬레이션, 에이전트 기반 시뮬레이션, 시스템 다이내믹스 및 연속 시뮬레이션 기반 최적화의 개념과 적용 방법
• 정책 최적화를 위한 강화학습의 기본 개념과 적용 방법

상기 학습 내용을 바탕으로, 학생들은 개인 프로젝트를 통해 자신의 전공 또는 관심 분야의 문제를 시스템으로 모델링하고, 이를 최적화 문제로 정식화한 뒤, 적절한 최적화 방법을 적용하여 최적해를 도출한다.

This is an introductory course in data science.

Through lectures and team projects, students will learn how to:

• write Python programs for working with datasets;
• wrangle data using NumPy, pandas, and SQL;
• conduct exploratory data analysis using descriptive statistics and visualizations;
• apply inferential statistics to estimate population parameters from sample data;
• apply dimensionality reduction to create more efficient feature representations for analysis; and
• apply foundational methods in regression, classification, and clustering to build predictive models and extract insights.

No prior knowledge of Python, Python data science libraries, statistics, or machine learning is required.

The course will cover the necessary basic concepts through lectures. However, because the course covers a broad range of topics, students are expected to learn actively and devote sufficient time to studying.

This is an introductory course in data science.

Through lectures and team projects, students will learn how to:

• write Python programs for working with datasets;
• wrangle data using NumPy, pandas, and SQL;
• conduct exploratory data analysis using descriptive statistics and visualizations;
• apply inferential statistics to estimate population parameters from sample data;
• apply dimensionality reduction to create more efficient feature representations for analysis; and
• apply foundational methods in regression, classification, and clustering to build predictive models and extract insights.

No prior knowledge of Python, Python data science libraries, statistics, or machine learning is required.

The course will cover the necessary basic concepts through lectures. However, because the course covers a broad range of topics, students are expected to learn actively and devote sufficient time to studying.

The main objective of this course is to have students explore a problem of interest. There are several secondary objectives for this course that includes learning research methodologies, developing presentation skills, enhancing writing skills, submitting deliverables in a timely manner, and successfully interacting with a research advisor. In a nutshell, this course basically provides an opportunity for students to develop problem-solving, critical thinking, and managerial skills by exploring the problem of interest. This course also features group projects that help students explore real-world problems in preparation for both industry jobs and advanced courses in graduate programs. After passing this course, students are expected to be able to identify, research, model, and analyze a relevant problem of interest.

Part I: Fundamentals and Practice of Reinforcement Learning

• Understand the principles of reinforcement learning algorithms based on Markov Decision Process (MDP) and Bellman equations.
• Implement and apply representative reinforcement learning algorithms (Q-learning, SARSA, DQN, A2C, DDPG, and PPO) in hands-on practice using simple environments.
• The course is based on Python programming and utilizes PyTorch.

Part II: Fundamentals and Practice of Computer Vision

• Understand and implement the fundamentals of image processing, including image representation, processing, and Fourier transforms.
• Understand and implement both traditional and deep learning-based computer vision techniques for feature extraction, feature matching, feature tracking, image classification, object detection, and image segmentation.
• Learn camera models and photogrammetry to understand and implement techniques for reconstructing real-world 3D information from two-view 2D images.
• The course is based on Python programming and utilizes OpenCV and PyTorch.

This course is an introductory foundational course for the data science.

• It provides an overview of core concepts and hands-on practice in data collection, wrangling, and analysis.
• Students will learn fundamental machine learning techniques and apply them to real-world datasets.
• The course also introduces, at a conceptual level, the fundamentals of relational databases, big data platforms, and data ethics.

머신러닝·빅데이터 분석에 필요한, 선형대수학/미적분학/통계학의 기초 개념을 학습한다.

• 선형대수학 (행렬, 연립방정식, 선형사상, 해석기하, 정사영, 행렬분해, 특이값분해)
• 미적분학 (벡터미분, 기울기)
• 통계학 (확률, 확률분포, 요약통계)
• 최적화 (최적화, 제약최적화)

The main objective of this course is to have students explore a problem of interest. There are several secondary objectives for this course that includes learning research methodologies, developing presentation skills, enhancing writing skills, submitting deliverables in a timely manner, and successfully interacting with a research advisor. In a nutshell, this course basically provides an opportunity for students to develop problem-solving, critical thinking, and managerial skills by exploring the problem of interest. This course also features group projects that help students explore real-world problems in preparation for both industry jobs and advanced courses in graduate programs. After passing this course, students are expected to be able to identify, research, model, and analyze a relevant problem of interest.

This course introduces the core principles of system design and optimization.

• Topics include problem formulation, experimental design, surrogate modeling, uncertainty and sensitivity analysis, simulation-based methods, and a wide range of optimization techniques.
• Students will build both a theoretical foundation and practical skills for applying these methods to solve design optimization problems across diverse disciplines.

In this course, students will learn fundamental skills to handle, analyze, and visualize datasets using the Python programming language, in order to extract meaningful insights from big data.

This course explores the core concepts of AI convergence and data science, as well as the latest issues and trends in related fields. Through this course, students will gain an understanding of what artificial intelligence and big data are before choosing these areas as a major or pursuing more advanced study. They will also learn how mathematics, statistics, and AI algorithms are applied in artificial intelligence and data science. In addition, students will have the opportunity to experience and utilize AI-based services that can be used without coding. In the latter part of the course, invited talks by experts from various fields will be offered, allowing students to learn firsthand how artificial intelligence and big data are applied and utilized across different domains.

본 수업은 1, 2학년을 대상으로 한 개론 수업으로서 ICT창업학부의 교수님들의 각 전문 분야에 대한 강의와 실습을 통해 ICT 융합에 필요한 다양한 기술에 대해 이해하고 경험해보는 것을 목표로 합니다. 이와 관련하여 본 수업에서 다루고자 하는 분야는 아래와 같습니다:

• 인간공학 (Human Factors Engineering: HFE)
• 인공지능 (Artificial Intelligence: AI)
• 빅데이터 분석과 AI 기술활용
• 컴퓨터 그래픽스 (Computer Graphics: CG)

이러한 다양한 분야에 대한 경험을 통해, 본 수업은 학생 각자의 관심분야에서 ICT를 활용한 융합을 수행하도록 동기를 부여하고 ICT 융합에 대한 아이디어 개발 및 기획 능력을 제고시키는 것을 목적으로 합니다.