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Lecture 01 - The Learning Problem

caltech

220K subscribers • 2 videos on Hallway

YouTube
  • Recommended by 5 people: 5 on Hacker News.
  • A classic. People kept recommending it from 2016 to 2023.
  • Presented by Yaser Abu-Mostafa, Professor at Caltech
  • May be out of date. Uploaded 14 years ago, in an area where tools change fast.

Beginner. No background in the topic needed.

1.4M views14 years ago5 recommendations

AI and machine learningComputer scienceClassic machine learningMath for machine learning

Yaser Abu-Mostafa introduces the fundamentals of machine learning, covering supervised, unsupervised, and reinforcement learning paradigms.

The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem. Lecture 1 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on April 3, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.

Why it's here

5 people recommend this

What people say. Recommended as an exceptionally clear and elucidating introduction to machine learning concepts and models.

xtiansimonon Hacker News • Recommends

On “A Curated List of AI and Machine Learning Resources from Around the Web”

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: link to this video

bob1029on Hacker News • Recommends

On “Ask HN: I am a full stack developer, where do I start learning for AI”

I have found the Caltech machine learning intro course to be the most elucidating of any resource so far: link to this video The delivery of the lecture material was like butter to me. Might work differently for others though.

weishigonameon Hacker News • Recommends

On “Ask HN: What is your favorite YouTube channel for developers?”

there are some lectures about machine learning on youtube. I think they are good to watch: link to this video and link and my favorite channels are Standford and MIT open courseware link and link

bonkerfied23on Hacker News • Recommends

On “Ask HN: How to Seriously Start with Machine Learning and AI”

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: link to 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 :)

GoldenMonkeyon Hacker News • Recommends

On “Ask HN: In 2023 which is the best path to get good at machine and deep learning?”

Caltech machine learning intro course: link to 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

u/fsharperin r/haskell • Mentions

On “call for collaboration on formalizing statistical learning”

I think that there is no mystery in machine learning from the formal point of view. AFAIK. ML algorithms are pure functions of many zillions of variables. This course among other things describe the functions implied in some ML algorithms The thing that destroy the purity of such algorithms are the effects needed for managing, selecting, filtering, analyzing and computing efficiently such big amount of parameters.

selimthegrimon Hacker News • Mentions

On “A Stark Nuclear Warning”

link link to 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.