Introduction to AI Principles: Understanding the Fundamentals

Have you been using LLMs only superficially? Gain a proper understanding of everything from how ChatGPT, Claude, and Gemini work to the essence of prompt design, then use those principles to collaborate with AI and create your own web novel from planning to completion. We’ll help you develop the ability to work with AI based not on “intuition,” but on “principles.”

HIPS : Human Intelligence Problem Solving, Although AI seems to replace everything, the ability to reframe complex problems and gain insight is uniquely human. We present methods for proactively utilizing AI and exercising uniquely human problem-solving capabilities.

With ChatGPT, create your own content (songs, comics, blogs, books, papers, etc.) and create new businesses.

Do you feel stuck every time you have to write a report? Wasting time not knowing what to write, revising over and over because the logic isn't clear, and barely finishing right before the deadline—this is a routine many professionals and students repeat. You may have tried using ChatGPT, but it often stops at simply writing sentences for you. It might produce "plausible writing," but it doesn't create a "result-driven report." This lecture is different. We will show you how to use ChatGPT not just as a task-delegation tool, but as a strategic partner that amplifies your planning capabilities. ✔ How to clearly structure problem definitions ✔ How to apply frameworks to design logical flow ✔ How to strengthen persuasiveness based on data ✔ Strategies to upgrade to performance-oriented reports During the lecture, you will actually complete one of your own work reports, providing a practical experience that leads to immediate results rather than simple theory. Furthermore, by utilizing the latest features like Canvas and file upload analysis, we help you build AI literacy to stay ahead in the rapidly evolving generative AI environment. After taking this course: Your report writing time will decrease, Your logic will become more solid, And your results will become clearer. Experience the moment when report writing is no longer a burden, but a weapon that proves your capabilities.

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.

"The deep learning code runs, but why do the formulas in papers look so unfamiliar?" As you study AI, you eventually hit a wall of mathematical formulas. If you trace the source of that wall, it usually leads back to not having a solid foundation in the most basic mathematical language. Starting with the language of sets, this course covers core concepts across 18 lectures, including equations, functions, sequences, exponents/logarithms, and trigonometric functions. We don't make you memorize formulas. While we briefly touch upon how these connect to the context of AI, the primary goal of this course is one thing — to build an unshakable foundation for when you later learn linear algebra, calculus, and probability/statistics. As the first part of the Suminjeongeum series, this course will provide you with the common language needed to properly digest the remaining three parts.

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.

The Gemini agent that works across Google Drive! Put the finishing touches on your Google AI workflow with overwhelming productivity.

ChatGPT, which used to only answer questions, now works directly on my computer. I give it instructions in Korean without typing a single line of code and receive the results as files. This six-part series, including the orientation, explains Codex’s workflow of context, planning, execution, and deliverables, then covers everything from safely defining a working folder to organizing files, creating tables with sources, and setting up scheduled executions that run automatically at a specified time.

No Coding Skills Required: Build Your Very First Service with Google Stitch An All-in-One AI Development Workflow That Seamlessly Connects Every Step from Planning to Deployment

This is an introductory course where you learn the core concepts of Claude Code through a 3D office analogy. From hiring a single robot in an empty office, giving it a brain and a heart, equipping it with tools, permissions, and memory, to eventually having multiple robots work together as a team—each concept unfolds as an office scene. You will master every concept by interacting with the digital course materials and complete your learning through hands-on practice on your own computer.

We will explore the types of applications and web services that can be built using LLMs.

WordPress Post Scheduling, Thumbnail Auto-Registration Program Code - Get Yours!

This is a practical course where even beginners can learn how to quickly create PDF and Notion templates using AI and generate passive income by selling them on platforms like Etsy, Gumroad, and the Notion Marketplace. You will master everything at once, from market research and production to creating product detail pages, uploading, and sales strategies.

You will learn about Transformers inside out by directly implementing everything from Multi-Head Attention to the Original Transformer model, BERT, the Encoder-Decoder-based MarianMT translation model, and the Vision Transformer in code.

Processing actual smart health data held by Daegu Haany University for use in machine learning, and creating customized smart health applications using vibe coding and refined information.

This course is ideal for those who want to focus on theory and context rather than practice, aiming to organize their knowledge of deep learning in depth and grasp the big picture. It makes the mathematical and statistical foundations of deep learning easy to understand and provides intuitive interpretations of key modern models—such as AutoEncoder, GAN, Transformer, and AlphaGo—from a developer's perspective.

We will examine the major OT security threats that can occur at production sites in the food and pharmaceutical industries, focusing on real-world cases. You will understand what security issues can arise in smart factories, production facilities, quality control systems, and traceability management processes, and learn basic response methods to prevent production shutdowns and data falsification in a way that is easy for beginners to follow.

How does the architecture change when an LLM is integrated into an existing backend? This is an introductory system design course that goes beyond simple LLM API calls to help you understand core AI service components such as Model Routers, LLM Gateways, and Vector DBs, and step-by-step design an operational AI service architecture including RAG and AI Agents. It is designed to help those who want to create their own LLM-based AI services quickly master the design workflow.

🔥Don’t have time to complete the 32-hour all-in-one course? We’ve condensed the four subjects of the Big Data Analysis Engineer written exam into around 4 hours. Connect concepts directly to actual exam questions through modified past exam questions, and receive 150 pages of lecture materials plus 13 pages of final review notes for the exam room.