Are supervised and unsupervised learning still relevant today? [D]
Mirrored from r/MachineLearning for archival readability. Support the source by reading on the original site.
Hey everyone,
I'm trying to get a better sense of where classic ML fits in the current landscape, dominated by LLMs and deep learning. Are supervised and unsupervised learning still considered important skills/topics to learn in 2026, or have they become mostly a "foundations" step before moving to more advanced techniques?
Also, if you have any book recommendations for Python that cover these topics well, I'd really appreciate it.
Thanks in advance!
[link] [comments]
More from r/MachineLearning
-
A collision-entropy floor for watermark/retrieval AI-text detection. Looking for a sanity check before I take this further [D]
Aug 14
-
TMLR Relevance and Prestige [D]
Aug 13
-
Reproducible canvas-aligned low-level patterns in somerandomllm-generated images and their possible relation to iterative editing artifacts [D]
Aug 13
-
worldproof: diagnosing where world-model predictions break and a measurement of when pixel metrics stop being able to rank models at all [P]
Aug 13
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.