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.

This course covers the fundamental concepts of linear algebra, calculus, and statistics that are essential for machine learning and big data analysis.

• Linear Algebra (matrix, system of linear equations, linear transformation, analytic geometry, orthogonal projection, matrix decomposition, singular value decomposition)
• Calculus (vector differentiation, gradients)
• Statistics (probability, probability distribution, descriptive statistics)
• Optimization (optimization methods, constrained optimization)

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.

This course is an introductory course designed for first- and second-year students. Through lectures and hands-on practice delivered by faculty members of the School of AI Convergence in their respective areas of expertise, the course aims to help students understand and experience various technologies essential for ICT convergence. The fields covered in this course include the following.

• Human Factors Engineering: HFE
• Artificial Intelligence: AI
• Big Data Analytics and AI Applications
• Computer Graphics: CG

By experiencing these diverse fields, the course aims to motivate students to pursue ICT-based convergence in their own areas of interest and to enhance their ability to develop and plan innovative ideas related to ICT convergence.