機器學習的數值方法論

📘 STOP BROWSING & ENROLL IN THIS CLASS! 📘 別再猶豫了，這門課就是為你而設！ This is the course for you! 如果你正在選課，這就是你要找的課程！ 💻 Fully online — No in-person meetings required 💻 全線上課程 — 不需到課 🎥 Lectures are recorded and available on YouTube (watch anytime) 🎥 課程錄影提供於 YouTube（可隨時觀看） 🧠 Every two weeks, you’ll work on a group challenge and present your results 🧠 每兩週一次小組挑戰，並於課堂中分享成果 ❌ No final exam, no midterm, no homework ❌ 沒有期末考、沒有期中考、沒有作業 ⏱️ Only 2 hours/week commitment ⏱️ 每週僅需 2 小時投入 🗣️ Each week includes ~20 minutes of lecture; the rest is student presentations 🗣️ 每週約 20 分鐘講課，其餘時間為學生報告與討論 📘 Course Description This course skips traditional lectures and homework to focus on three things that actually matter when working in machine learning: teamwork, self-learning, and knowing how to use GenAI tools. Every two weeks you’ll receive a numerical computing challenge related to machine learning — optimization, matrix factorization, probabilistic modeling, etc. You’ll work in a group to solve it, and present your solution in class. The tasks are hard on purpose. You can’t complete them alone — you’ll have to rely on your teammates and explore the topic independently. Generative AI tools are encouraged. They're new, evolving, and there’s no manual. This course is your chance to understand what they can and can’t do. Students usually finish this course with two realizations: (1) group work is hard, and (2) real-world ML is about coding, debugging, and solving problems no one has solved for you. Check out the YouTube playlist with the course from last semester https://www.youtube.com/playlist?list=PLb1V9aVV3FkHNpORbJzpNM_nciM_RxVnp

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1.Homework and Assignments, Exams and Quizzes, Evaluation and Grading Policy： 🧰 Course Tools & Frameworks Challenge-based learning framework description https://docs.google.com/document/d/1GA4DIyrkDZwJ0Msq9I4vGjyd3gN6DdntQ_x-L7NBimE/edit?usp=sharing Calendar event with Google Meet link https://calendar.google.com/calendar/event?action=TEMPLATE&tmeid=MzI0dnJ0ZjlsMHM4OWcwdnRzamVhbXJqdjBfMjAyNjAyMjZUMDUyMDAwWiByaW5pLnN0ZWZhbm9AbQ&tmsrc=rini.stefano%40gmail.com&scp=ALL Slides of the course (stolen 😀) https://math.umd.edu/~mariakc/NumericalMethodsforDataScienceAndMachineLearning.html Slack channel for the course (E3 is terrible for class management...) https://join.slack.com/t/numericalmeth-p8s1252/shared_invite/zt-3os900taa-eyMESlLTejYF4pWQ4BQKog YouTube channel (subscribe to help me monetize my videos 💰) https://www.youtube.com/@srini2

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online online online