Sunghee Yun (He/Him)

Co-Founder & CTO @ Erudio Bio, Inc., Co-Founder & CEO @ Erudio Bio Korea, Inc., Leader of Silicon Valley Privacy Preserving AI Forum (K-PAI), CGO / Global Managing Partner @ LULUMEDIC, KFAS-Salzburg Global Leadership Initiative Fellow @ Salzburg Global Seminar, AI-Korean Medicine Integration Initiative Task Force Member @ The Association of Korean Medicine, Director of AI Semiconductor @ K-BioX, Visiting Professor @ Sogang University, Advisory Professor @ DGIST, Global Advisory Board Member @ Innovative Future Brain-Inspired Intelligence System Semiconductor of Sogang University, Network Expert @ Gerson Lehrman Group, Chief Business Development Officer @ WeStory.ai, and Advisor @ CryptoLab, Inc.

Abstract

Embarking on an AI/ML journey requires not just motivation, but a strategic roadmap. This lecture addresses the fundamental question: “What’s the best way to learn AI/ML?” We’ll explore proven learning methodologies combining structured education (online courses, books, college classes) with hands-on practice. You’ll discover why coding proficiency (especially Python), git workflow mastery, and personal projects are non-negotiable for serious AI practitioners, and how online courses have democratized access to world-class AI education.

The mathematical prerequisites for ML—linear algebra, calculus, and statistics—often intimidate beginners. We’ll demystify these foundations by focusing on the essential concepts that directly impact your ML work: matrix operations, vector calculus, gradient computation, probability distributions, and statistical inference. Through concrete examples tied to machine learning algorithms, you’ll see how these mathematical tools power everything from backpropagation to optimization, understanding not just the “what” but the “why” behind the formulas. The session includes detailed walkthroughs of ML fundamentals, from problem formulation through supervised, unsupervised, and reinforcement learning paradigms to the mechanics of training deep neural networks.

In the practical portion, we’ll explore ML in action—defining business problems, data engineering, feature engineering, and model deployment strategies. Most importantly, we’ll demonstrate how Large Language Models can revolutionize your learning process: using LLMs to explain complex mathematical concepts, debug your understanding, generate practice problems, and create personalized learning materials. Whether you’re a student, a professional transitioning into AI, or someone looking to strengthen their foundations, this lecture provides both the technical depth and practical wisdom to accelerate your AI/ML mastery.