CourseAI

This course covers elementary discrete mathematics for science and engineering, focusing on mathematical tools and proof techniques useful in computer science. Learners will explore topics such as logical notation, sets, relations, graph theory, state machines, and algorithms.

This course delves into projection theory, exploring how a set behaves under various orthogonal projections. Learners will engage with both classical techniques and contemporary applications across multiple mathematical disciplines, including harmonic analysis and analytic number theory.

This course focuses on learning several rules to improve speaking skills in important situations. Learners can master effective presentation methods for various contexts, such as interviews, thesis defenses, and oral exams.

This course introduces the mathematical modeling of computational problems and the common algorithms, algorithmic paradigms, and data structures used to solve them. It emphasizes the relationship between algorithms and programming, and introduces basic performance measurement and analysis techniques.

This course focuses on the close analysis and criticism of a wide range of films, from early silent cinema to classic Hollywood genres and European and Japanese art cinema. Students will compare films from different eras and countries to interpret in-depth analyses and explore theoretical issues related to audiences.

This course covers the fundamental principles of finite element analysis for solving complex static and dynamic problems. Learners will learn how to implement finite element procedures for linear and nonlinear analysis.

This course covers the fundamental principles of biochemistry, genetics, molecular biology, and cell biology. It places a particular emphasis on understanding the role of microorganisms in ecosystems and human health.

This course introduces the fundamental principles of biochemistry, molecular biology, and genetics to understand the functions of living systems. Learners will acquire the knowledge necessary to understand human health and therapeutic interventions through examples from chemical biology and 21st-century molecular genetics.

This is a foundational chemistry course for students with a strong background in chemistry. It focuses on the basic principles of the electronic structure of atoms and molecules, thermodynamics, acid-base and redox equilibria, chemical kinetics, and catalysis.

This course provides an introduction to nuclear science and its engineering applications. It explains basic nuclear models, radioactivity, nuclear reactions, and kinematics, and covers the interaction of ionizing radiation with matter.

This course covers differential equations and the MATLAB® ODE suite in depth. Learners can understand the principles and applications of differential equations and develop problem-solving skills using MATLAB.

This course covers the foundations of calculus, primarily including content taught in introductory calculus courses for freshmen. It allows learners to review core concepts of calculus and provides useful materials even for students learning it for the first time.

In this course, students learn the complexities of developing video games in small, multidisciplinary teams. They will master creative design and production methods, and learn how to design, develop, and thoroughly test their own digital games in collaboration with team members.

This course covers the content necessary to understand and create machine learning algorithms, specifically deep learning and neural networks, through linear algebra concepts. It provides a comprehensive explanation of linear algebra, including applications in probability, statistics, and optimization.

This course provides an interactive introduction to discrete mathematics for computer science and engineering. Learners will study fundamental mathematical concepts, discrete structures, and discrete probability theory, developing the ability to apply these to various fields of computer science.

This course covers the fundamental principles of biochemistry, genetics, molecular biology, and cell biology. In particular, you will learn about biological functions at the molecular level, as well as the structure and regulation of genes and proteins.

This course emphasizes the structural principles of digital systems and introduces architectures commonly applied across various technologies. Learners will cover a wide range of topics, including multi-level implementation strategies, the definition and mechanisms of new primitive elements, and performance measurement.

This course covers the basics and applications of the latest artificial intelligence technologies. Learners can develop problem-solving skills through AI principles and various use cases.

This course introduces the mathematical modeling of computational problems. It covers common algorithms, algorithmic paradigms, and the data structures used to solve these problems.

In this course, you will explore how matter is composed and why it behaves the way it does, learning how to understand the physical world through chemical intuition. Learners will gain an understanding of the electronic structure of matter and the various properties that arise from it.

Learn how to build deep learning models yourself using intuitive and Pythonic PyTorch. This reflects the latest PyTorch version.

This lecture is for those who have no knowledge of artificial intelligence, machine learning, deep learning, or data analysis but would like to experience it. I will explain the entire process.

From Multi-Head Attention to the Original Transformer model, BERT, the Encoder-Decoder based MarianMT translation model, and even Vision Transformer, you'll learn Transformer inside and out by implementing them directly in code.

In the second season of Professor Kim Seong-hun's "Deep Learning for All" course from Hong Kong University of Science and Technology, we will learn about "reinforcement learning."

Data Science(DS) & Artificial Intelligence(AI) job role analysis, plus a learning & employment roadmap for building a career in these roles.

This course is a comprehensive program that covers everything you need to know to utilize generative AI in practical work settings, from the basic principles of LLM (Large Language Models) to RAG (Retrieval-Augmented Generation), and even the latest technologies like AI Agent and MCP (Modular Command Protocol). It is structured to naturally teach you how generative AI understands, searches, judges information, and extends into AI Agents that take action, following the flow of technological development.

Understand the basic concepts and latest trends of big data, and through technical understanding of big data analysis techniques, extract data patterns to present methods for business application. Through big data, you can create new businesses and develop insights into future business environments.

Build your own powerful stock price analysis model that analyzes over 40 types of economic indicators and stock price data using AI to predict not only the S&P 500 and QQQ ETFs but also individual stocks!

This course is an introductory program designed so that learners who are new to AI technology can easily understand and follow along. Through the lectures, you will learn the basic concepts and usage methods of AI platforms, and conduct hands-on practice directly generating images using various AI tools. Upon completing this course, you will become much closer to AI and acquire the capability to create AI images that can visually implement creative ideas.

This is a machine learning lecture written in easy-to-understand language, like a story, without any formulas—tailored perfectly for those who are humanities students to the core!

Time is constantly flowing, and data is constantly accumulating. In this dynamic world, time series data plays a vital role in every aspect of our lives. From the volatility of financial markets to subtle signals of climate change, time series data captures it all. Now learn how to interpret and leverage this powerful data using the power of Python and deep learning!

Starting with the question, "If I were on the Titanic, would I have survived?", this course completes a full-stack project where you develop an AI model to predict survival probabilities based on real data and deploy it as a web service. You will gain hands-on experience in the entire process of AI and web development, from deep learning modeling using PyTorch to building a backend server with FastAPI and implementing a user interface with Next.js.

This course is a hands-on masterclass that completely dissects the core technological evolution of generative AI, from LDM (Latent Diffusion Model) to DiT (Diffusion Transformer). We directly analyze the latent space-based learning principles of LDM, the structure of Stable Diffusion, and the implementation methods of the latest Diffusion Transformer through papers and code. Students will systematically learn the latest trends and structural evolution of generative models by directly implementing LDM, CFG (Classifier-Free Guidance), and DiT models using PyTorch.

A general education lecture for the public to shed the vague fear of artificial intelligence. Through concrete examples, we learn what AI learning truly means. Also, via specific examples, we examine AI's 'brain structure' and how it thinks.

Essential certification for the AI era, MS AI-900! 🚀 Did you know? ChatGPT operates exclusively on Microsoft Azure! If you want to properly utilize Microsoft Azure OpenAI, which exclusively provides the latest GPT models, now is the perfect opportunity. Grow into an Azure AI services expert through the AI-900 course and master enterprise-grade AI solutions that companies trust. Secure global official certification and enhance your competitiveness as a true AI professional!

An introductory AI lecture tailored for those new to artificial intelligence! It easily summarizes core concepts from data to generative AI. The goal is not to build AI, but to achieve a level of practical understanding where you can ask the right questions and explain the technology.

This course is a hands-on masterclass that completely conquers the evolution of Diffusion Models through papers and code implementation. You'll learn the core models of generative AI, including DDPM (Denoising Diffusion Probabilistic Model) and DDIM, by studying the paper principles and implementing them directly. We analyze step-by-step the background of each model's emergence, mathematical formulations, network architectures (U-Net, VAE, Transformer), training processes (Noise Schedule, Denoising Step), and the ideas that led to performance improvements. Students will directly code all models using PyTorch, gaining not just paper comprehension but 'practical skills to reproduce and apply' them in real-world scenarios. Additionally, by comparing the differences between models and their developmental flow, you'll clearly understand how they expand and evolve. This course integrates theory, code, and practice into one comprehensive journey, providing researchers, developers, and creators alike with a systematic way to master the evolution of generative models. Beyond simply 'reading' papers, start your experience of 'understanding and recreating' through direct implementation now.

This course explains the basic concepts of LLM and GPT in an easy-to-understand way for everyone. Students will build their own chatbot using the GPT API and deploy it on the web using Streamlit. By experiencing everything from the basics to hands-on practice, you can build a solid foundation for your first steps in utilizing AI.

From core theories of deep learning and CNN to various CNN model implementation methods, and practical deep learning development know-how through real-world problems, If you want to become a deep learning CNN technology expert based on Pytorch, join us in this lecture :)

The electronics and semiconductor industry is a field where data-driven management and innovation are particularly crucial due to ultra-precision processes and complex supply chains. This course covers practical strategies that can be directly applied in electronics and semiconductor manufacturing, including defect detection, process optimization, predictive maintenance, and supply chain management using AI technology. Along with real-world cases from global companies, the course also presents low-cost, high-efficiency AI implementation methods that small and medium-sized enterprises can realistically utilize. Through this, students will be able to understand and apply AI-based manufacturing strategies that not only improve productivity and reduce costs, but also build future competitiveness.