Stanford CS230 | Autumn 2025 | Lecture 2: Supervised, Self-Supervised, & Weakly Supervised Learning

ℹ️ EPISODE INFO

Released

October 7, 2025

Duration

1h 39m

Channel

Stanford Online

Watch on YouTube

▶ Open ↗

📝 EPISODE DESCRIPTION

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai September 30, 2025 This lecture covers key AI concepts through case studies. To learn more about enrolling in this course, visit: https://online.stanford.edu/courses/cs230-deep-learning To follow along with the course schedule and syllabus, visit: https://cs230.stanford.edu/syllabus/ More lectures will be published regularly. View the playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rNRRGdS0rBbXOUGA0wjdh1X Andrew Ng Founder of DeepLearning.AI Adjunct Professor, Stanford University’s Computer Science Department Kian Katanforoosh CEO and Founder of Workera Adjunct Lecturer, Stanford University’s Computer Science Department

🎤 SPEAKERS

• Kian Katanforoosh

  host

  Co-creator and co-lecturer of Stanford CS230 and CEO of Workera (AI-based skills measurement).

🧾 EPISODE SUMMARY

In this episode of Stanford Online, featuring Kian Katanforoosh, Stanford CS230 | Autumn 2025 | Lecture 2: Supervised, Self-Supervised, & Weakly Supervised Learning explores practical deep learning decisions: supervision types, embeddings, and project tradeoffs The instructor frames deep learning as an engineering decision process—choosing data, labels, capacity, resolution, architecture, and especially loss functions to match real-world constraints.

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