| Date | Slides | Week and topic | Homework and logistics |
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- No formal prerequisites. Curiosity required. 공식 선수 과목 없음. 호기심 필수.
Course Objectives:
- Master core MongoDB data modeling, querying, and administration skills to a production-ready level.
- Understand how MongoDB’s document model and Atlas Vector Search support modern AI applications, specifically Retrieval-Augmented Generation (RAG).
- Apply performance and scaling principles (indexing, sharding, replication, real-time pipelines) to realistic, high-volume workloads.
- Design, build, and present a complete AI-powered application with a MongoDB backend.
Key Topics:
- Part 1, Weeks 1–8 (Foundations): document model & architecture, developer tooling, schema/index design, CRUD & aggregation queries, administration, security, Atlas cloud deployment.
- Part 2, Weeks 9–15 (AI & Big Data): Atlas Vector Search, RAG architecture, performance-oriented schema design at scale, real-time pipelines (Change Streams), horizontal scaling (replication/sharding), monitoring.
Course Methodology and Assessment:
“Follow-me” live coding each session → immediate individual lab applying the same pattern to a new dataset → weekly/biweekly homework. Midterm (Week 8, written + hands-on practical). Open-ended team capstone (Weeks 8–14, proposal → checkpoint → demo). Final exam (Week 15, cumulative, weighted toward Part 2).
Aaron Snowberger earned his Ph.D. in Information and Communications Engineering from Hanbat National University in South Korea in 2023. He also holds degrees in Computer Science and Media Design. He has taught technology courses for over 8 years, English for over 15 years, and has freelanced as a web developer and magazine designer for over 5 years. His current research interests include computer vision, natural language processing, image processing, signal processing, and machine learning.
Aaron Snowberger는 2023년 한국 한밭대학교에서 정보통신공학 박사 학위를 취득했습니다. 그는 또한 컴퓨터 과학 및 미디어 디자인 학위를 취득했습니다. 그는 8년 이상 기술 과정을 가르쳤고, 15년 이상 영어를 가르쳤으며, 5년 이상 웹 개발자 및 잡지 디자이너로 프리랜서로 일했습니다. 현재 연구 관심사는 컴퓨터 비전, 자연어 처리, 영상 처리, 신호 처리, 기계 학습입니다.