caltech
5 HN recommenders · recommended in 6 months, 2016–2023
1:21:28 · 1.5M views · Aug 28, 2012
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.
Recommended repeatedly in 'where do I start with ML' threads as a clear, math-aware introduction.
6 comments from Hacker News
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: this video The delivery of the lecture material was like butter to me. Might work differently for others though.
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: 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]
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]