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We would like to inform you about the AI online courses currently offered by KAIST.

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[Beginner(Stage 1)] Introduction to Programming with Python (CS101)

  • This course is designed for those who are starting programming for the first time, to develop software using the Python language. The course content is structured to help you acquire computational thinking skills that systematically define problems and create step-by-step solutions. In addition to simple Python programming language syntax, it provides a practical, case-study-centered learning approach focusing on various applications.
  • https://online.kaist.ac.kr/courses/66d16fb48004c3e1b006ae2e

[Beginner(Stage 1)] Practical Python for AI Coding

  • This course may be somewhat limited in scope due to the selection of recurring Python code that an economist recognized during the course of learning and teaching AI coding with Python, but the goal is to provide students with the fundamentals of object-oriented programming. After completing the course, students will have the practical skills to jump right into AI coding with scikit-learn and TensorFlow. This class uses Anaconda to install Python and the included JupyterLab to practice Python. JupyterLab provides a learning environment that allows you to manage directories and files more conveniently than simply utilizing Jupyter Notebooks.
  • https://online.kaist.ac.kr/courses/66d16fb88004c3e1b006ae56

[Beginner(Stage 1)] Data Science Walk

  • This course is designed for individuals who are new to the concept of data. The goal is to provide a light introduction to the theoretical background and procedures of data analysis, making the field of "data science" accessible to everyone.
  • https://online.kaist.ac.kr/courses/66d16fb58004c3e1b006ae3a

[Beginner(Stage 1)] Digital Transformation

  • This course provides various examples of digital transformation that are being used in real industries by subject. This course explains various technologies and digital innovations in a case-oriented way to be accessible without a theoretical background, and for this purpose, it is organized in a talk format between the moderator and the instructor, rather than a one-sided delivery of the contents of the lecture by individual instructors. In addition, for those who are professionally curious about actual AI technology or digital-related technology, domestic researchers directly study and deal with cases applied to the industry.
  • https://online.kaist.ac.kr/courses/66d16fb98004c3e1b006ae62

[Beginner(Stage 1)] Smart Innovation Manufacturing for Non-AI Majors

  • The course "Smart Innovation Manufacturing for Non-AI Majors" covers what smart manufacturing is, how it differs from traditional manufacturing methods, and how it has evolved. To understand these topics, students will learn about five key areas: 1) The Fourth Industrial Revolution and Innovative Manufacturing 2) AI-Based Manufacturing 3) Product Design and Manufacturing Based on Stanford Design Thinking 4) Factory as a Service (FaaS) using 3D Printing 5) Design for Additive Manufacturing (DfAM) for Smart Manufacturing
  • https://online.kaist.ac.kr/courses/66d16fbb8004c3e1b006ae70

[Foundation(Stage 2)] Introduction to Artificial Intelligence and Machine Learning 1

  • This lecture introduces theoretical knowledge of machine learning based on probability, statistics, and optimization. The process describes various probability theories and statistical methodologies, introduces optimization methods, and, Different models such as Naive Bayes, Logistic Regression, Support Vector Machine, Neural Network, Hidden Markov Model, Gaussian Mixture Model, and K-Means Model can be used to learn how to identify theoretical foundations for machine learning.
  • https://online.kaist.ac.kr/courses/66d16fb28004c3e1b006ae1e

[Foundation(Stage 2)] Introduction to Artificial Intelligence and Machine Learning 2

  • This lecture is provided following 'Artificial Intelligence and Machine Learning Introduction 1', and introduces theoretical knowledge about machine learning based on probability, statistics, and optimization. The process describes various probability theories and statistical methodologies, introduces optimization methods, and, Different models such as Naive Bayes, Logistic Regression, Support Vector Machine, Neural Network, Hidden Markov Model, Gaussian Mixture Model, and K-Means Model can be used to learn how to identify theoretical foundations for machine learning.
  • https://online.kaist.ac.kr/courses/66d16fb28004c3e1b006ae20

[Foundation(Stage 2)] Data Science Programming 1

  • This course introduces the basic core contents of data science programming. Data science programming is the study of parameter inference and sampling of the distribution of data. Through this course, you can build the necessary statistical foundations and study the key aspects of regression analysis and neural networks, which are simple models of machine learning. The class consists of a total of two lectures. In Data Science Programming 1, you will learn basic statistics such as data distribution, point estimation, confidence interval estimation, and bootstrapping, and learn about regression analysis, the simplest model in machine learning. Next, in Data Science Programming 2, you can study more in-depth content such as variable selection in polynomial regression analysis, and learn about dimensionality reduction and gradient descent, which are the core of neural networks.
  • https://online.kaist.ac.kr/courses/66d16fbd8004c3e1b006ae82

[Foundation(Stage 2)] Data Science Programming 2

  • This course introduces the basic core contents of data science programming. Data science programming is the study of parameter inference and sampling of the distribution of data. Through this course, you can build the necessary statistical foundations and study the key aspects of regression analysis and neural networks, which are simple models of machine learning. The class consists of a total of two lectures. In Data Science Programming 1, you will learn basic statistics such as data distribution, point estimation, confidence interval estimation, and bootstrapping, and learn about regression analysis, the simplest model in machine learning. Next, in Data Science Programming 2, you can study more in-depth content such as variable selection in polynomial regression analysis, and learn about dimensionality reduction and gradient descent, which are the core of neural networks.
  • https://online.kaist.ac.kr/courses/66d16fbd8004c3e1b006ae84

[Intermediate(Stage 3)] Math for AI Beginner Part 1: Linear Algebra

  • This is basic math for AI non-majors. It's an easy and fun linear algebra course, so please pay a lot of attention.

[Intermediate(Stage 3)] Math for AI Beginner Part 2: Vector Calculus

  • This course introduces fundamental AI-related mathematical concepts and techniques necessary for formulating and solving engineering problems using AI. It covers AI concepts, the Fourth Industrial Revolution, machine learning, and applications utilizing vector calculus. The course introduces gradient, divergence, curl, and related identities for vector point functions, including evaluation and verification of line, surface, and volume integrals using Gauss’s, Stokes’s, and Green’s theorems. Applications of partial differential equations in various research fields for AI and mathematics beginners are also included.
  • https://online.kaist.ac.kr/courses/67c947f274459ef75d0a1066

[Intermediate(Stage 3)] Modeling and Simulation Introduction 1

  • This course introduces various formalisms and modeling methodologies in modeling and simulation. Modeling and simulation involve creating a virtual world within a computer to replicate real-world problems and explore potential solutions. It is a process of specifying models tailored to certain purposes for phenomena that rarely occur in reality or are challenging to experiment with due to various reasons. The course covers the entire process, from specifying the model and implementing it in a simulation to conducting statistical analysis using the implemented simulation.
  • https://online.kaist.ac.kr/courses/66d16fbc8004c3e1b006ae7c

[Intermediate(Stage 3)] Reinforcement Learning 1

  • Reinforcement learning is a powerful learning method that teaches optimal decision-making in dynamic systems where the state changes according to the agent's choices. Since AlphaGo demonstrated that reinforcement learning can make decisions superior to those of humans, even in very complex problems, it has garnered much attention and become a rapidly advancing field. In the first half of this course, we will learn the mathematical foundations of reinforcement learning and traditional algorithms. In the latter half, we will study recent topics from papers, including deep reinforcement learning. We invite you to explore the world of reinforcement learning.
  • https://online.kaist.ac.kr/courses/66d16fba8004c3e1b006ae6a

[Intermediate(Stage 3)] Reinforcement Learning 2

  • Reinforcement learning is a powerful learning method that teaches optimal decision-making in dynamic systems where the state changes according to the agent's choices. Since AlphaGo demonstrated that reinforcement learning can make decisions superior to those of humans, even in very complex problems, it has garnered much attention and become a rapidly advancing field. In the first half of this course, we will learn the mathematical foundations of reinforcement learning and traditional algorithms. In the latter half, we will study recent topics from papers, including deep reinforcement learning. We invite you to explore the world of reinforcement learning.
  • https://online.kaist.ac.kr/courses/66d16fbb8004c3e1b006ae6c

[Intermediate(Stage 3)] Advanced Artificial Intelligence and Machine Learning 1

  • This course covers the various theories of artificial intelligence and machine learning, with a focus on deepening and real-world applications. For deepening, we introduce Bayesian nonparametric machine learning methods. For applications, we will explain how filtering and matrix factorization can be applied in the real world. By the end of this course, you will be able to theoretically apply nonparametric machine learning methodologies to regression, classification, and clustering, and you will be able to practically apply them to recommendation systems, filtering systems, etc. Finally, we will discuss the structure and parameter inference of neural network-based modeling and introduce some examples of its applications
  • https://online.kaist.ac.kr/courses/66d16fb38004c3e1b006ae28

[Intermediate(Stage 3)] Advanced Artificial Intelligence and Machine Learning 2

  • This lecture explores the structure of VAE, Objective Function, and the Reparameterization Trick. It also delves into various forms of VAE modifications, covering changes to Prior structures, Objective Function variations, and Prior Optimality adjustments, with case-by-case studies.
  • https://online.kaist.ac.kr/courses/66d16fba8004c3e1b006ae66

[Advanced(Stage 3)] Applications in Industrial and Systems Engineering

  • This course is case-based, covering key industrial and systems engineering topics with a variety of real-world industrial examples. The class topics are selected by selecting diverse yet fundamental cases that cover many of the key methods at the core of industrial and systems engineering practice. Many of the topics will be longer and more complex cases than the typical short cases covered in theory. (We will also introduce teaching methods using LEGOs.)
  • https://online.kaist.ac.kr/courses/66d16fb68004c3e1b006ae42

[Advanced(Stage 3)] Introduction to Optimization in Computer Vision

  • In this lecture, I will introduce optimization techniques that are widely used in image understanding (computer vision). It is a good starting point for researchers who need to understand the field of image understanding, as well as for those who want to understand the trends and theories of basic optimization techniques.
  • https://online.kaist.ac.kr/courses/66d16fb28004c3e1b006ae1c

[Advanced(Stage 3)] AI Materials

  • This course defines artificial intelligence (AI) as a machine to which we delegate some or all of the functions of the human brain. As AI evolves from simple calculators, it requires new materials and components, and this course examines what new materials are needed and how machine learning, derived from AI, can significantly accelerate the development of new materials.
  • https://online.kaist.ac.kr/courses/66d16fbc8004c3e1b006ae76

[Advanced(Stage 3)] AI in ME 1

  • The AI in Mechanical Engineering course, developed by the Department of Mechanical Engineering at KAIST, is now open to the general public. This course presents eight independent topic-based lectures that explore how AI is being applied to major challenges in mechanical engineering to generate real-world value. Biomechanics: Enhancing data quality of wearables and predicting human motion (Prof. Soogyong Park) Visual Perception: Computer vision and ML-based scene understanding (Prof. Gukjin Yoon) Vehicle Intelligence: Vehicle guidance, navigation, and control (Prof. Jinhwan Kim) Material Optimization: Inference and optimization of material properties (Prof. Seunghwa Yoo) Sound and Vibration: Acoustic recognition, event detection, and health sensors (Prof. Yong-Hwa Park) Fluid Mechanics: Reverse optimization of unknown variables and conditions (Prof. Hyungsoo Kim) Medical Simulation: Enhancing visuals of high-fidelity simulations (Prof. Dooyong Lee) Robot Control: Deep reinforcement learning and optimal control (Prof. Jaemin Hwangbo)
  • https://online.kaist.ac.kr/courses/66d16fb78004c3e1b006ae4a

[Advanced(Stage 3)] AI in ME 2

  • The AI in Mechanical Engineering course, developed by the Department of Mechanical Engineering at KAIST, is now open to the general public. This course presents eight independent topic-based lectures that explore how AI is being applied to major challenges in mechanical engineering to generate real-world value. Biomechanics: Enhancing data quality of wearables and predicting human motion (Prof. Soogyong Park) Visual Perception: Computer vision and ML-based scene understanding (Prof. Gukjin Yoon) Vehicle Intelligence: Vehicle guidance, navigation, and control (Prof. Jinhwan Kim) Material Optimization: Inference and optimization of material properties (Prof. Seunghwa Yoo) Sound and Vibration: Acoustic recognition, event detection, and health sensors (Prof. Yong-Hwa Park) Fluid Mechanics: Reverse optimization of unknown variables and conditions (Prof. Hyungsoo Kim) Medical Simulation: Enhancing visuals of high-fidelity simulations (Prof. Dooyong Lee) Robot Control: Deep reinforcement learning and optimal control (Prof. Jaemin Hwangbo)
  • https://online.kaist.ac.kr/courses/66d16fba8004c3e1b006ae64