1.5M views · Aug 28, 2012 · 1:21:28 · 5 HN recommenders · 6 mentions · discussed 2016–2023
The first lecture of Caltech's CS 156 Machine Learning course by Yaser Abu-Mostafa, introducing the learning problem and supervised, unsupervised and reinforcement learning. It is one of 18 lectures in the Learning from Data course.
Why Hacker News points people here: Recommended repeatedly in 'where do I start with ML' threads as a clear, math-aware introduction.
“I have found the Caltech machine learning intro course to be the most elucidating of any resource so far”HN comment, 2023
“one course I very much enjoyed auditing was "Learning from Data”
Enjoyable course with a fresh presentation of the learning model
I'm a neophyte to ML, but one course I very much enjoyed auditing was "Learning from Data" [1] taught by Caltech Professor Yaser Abu-Mostafa. I found concept of the _Learning Model_ particularly fresh representation of the ML activity (starting at 14:39 in the first video linked here [2]) [1]: link [2]: this video
“I have found the Caltech machine learning intro course to be the most elucidating of any resource so far”
Most elucidating ML intro resource, delivered clearly
I have found the Caltech machine learning intro course to be the most elucidating of any resource so far: this video The delivery of the lecture material was like butter to me. Might work differently for others though.
“there are some lectures about machine learning on youtube. I think they are good to watch”
Good machine learning lectures worth watching
there are some lectures about machine learning on youtube. I think they are good to watch: this video and link and my favorite channels are Standford and MIT open courseware link and link
“I like how he explains stuff and adds some context behind the math of each topic.”
Online lectures explaining the math and context behind each topic
Pattern Recognition and Machine Learning by Christopher Bishop. Book: link Notes: Its very very math heavy but if you really want to grasp the concepts and the idea around each topic, this is one of the way to go. Online Lectures: this video I like how he explains stuff and adds some context behind the math of each topic. Alternative: link A pretty good youtube channel that follows up on modern machine learning and he has all of his video tutorial demos on GitHub. Hope this helps fellow Machine Learner :)
“Caltech machine learning intro course”
Listed as a learning resource for ML
Caltech machine learning intro course: this video karpathy's Zero to Hero series ( link ) meta llama 2 - is open source link Tools: ai - hosting Banana - Machine Learning Model Deployment on Serverless GPUs link pinecone - vector database: link how to run AI language models on a single cpu pc - link
“He covers the VC dimension in”
Caltech Learning from Data course covering VC dimension
link this video (incidentally for graycat, Yaser Abumostafa is a Muslim Egyptian immigrant from Cairo) He covers the VC dimension in link , and leaves the proof to an appendix of the book.
