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#andrewng#cs229#supervisedlearning

Lecture 1 | Machine Learning (Stanford)

Stanford

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  • Recommended by 11 people: 11 on Hacker News.
  • A classic. People kept recommending it from 2008 to 2020.
  • Presented by Andrew Ng, Associate Professor at Stanford University
  • May be out of date. Uploaded 18 years ago, in an area where tools change fast.

Beginner. No background in the topic needed.

2.6M views18 years ago11 recommendations

AI and machine learningComputer scienceNeural networks and deep learningClassic machine learningAlgorithms

Andrew Ng provides an introductory overview of machine learning, covering supervised learning, unsupervised learning, learning theory, and reinforcement learning.

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: youtube.com/view_play_list?p=A89DCFA6ADACE599 CS 229 Course Website: stanford.edu/class/cs229/ Stanford University: stanford.edu/ Stanford University Channel on YouTube: youtube.com/stanford

Why it's here

11 people recommend this

What people say. Widely recommended as the gold standard introduction to the mathematical foundations of machine learning.

Moments people linked to

11001on Hacker News • Recommends

On “Free eBooks On Machine Learning”

I'd suggest going back to the original youtube1 and course materials2. Coursera version is nothing but a hand-wavy watered down "feel good" version of the original class. I also really like the Caltech's take "Learning from data"3 1 link to this video 2 link 3 link

schtogon Hacker News • Recommends

On “Stanford's "autonomous" helicopters teach themselves to fly”

Start leaning from the guy right now: link to this video Stanford class on machine learning by Andrew Ng, 20 lectures.

bravuraon Hacker News • Recommends

On “Ask HN: Good books on machine learning?”

…the study of bias. If you want to understand when to pick certain techniques, and develop appropriate biases, then read Bishop. "The Elements of Statistical Learning" by Hastie, Tibshirani and Friedman gives more of a statistician's approach. The treatment is simply less broad, and also more dated. You can also look at Andrew Ng's video lectures: link to this video He is very well-respected in the field. For certain students, watching a lecture may be preferable to reading a book.…

datageekon Hacker News • Recommends

On “Ask HN: Ideas for weekend projects?”

Build a better chess rating system and enter your system into the following competition: link . You may want to use machine learning techniques, which you can learn using the Andrew Ng's Stanford lectures ( link to this video ).

lawon Hacker News • Recommends

On “Free online version of Stanford's Fall 2011 Intro to AI course”

This reminds me of CS229, of which there's an online version: link to this video It's considerable more focused in scope, presenting the mathematics behind some of the more popular algorithms extensively used in machine learning, which is a subset of artificial intelligence. The course starts off pretty slow, but quickly gains speed and momentum. By the end, you should be fairly comfortable with clustering and classification/regression, among other topics. The lecture notes are also fantastic.

chedigitzon Hacker News • Recommends

On “Stanford Fall 2011 - Introduction to Databases”

Thanks for posting the other two course. I can't wait till October. The Machine Learning course has a great a playlist on youtube as well. link to this video

dvdhsuon Hacker News • Recommends

On “Introduction to Machine Learning”

One caveat though: the Coursera ML course is CS229A(pplied), which focuses on applying machine learning, rather than the mathematics behind it. The "real" CS229 lectures are on YouTube 1, and go much more in depth. 1. link to this video

bglazeron Hacker News • Recommends

On “CMU's Introduction to Machine Learning Course”

Nope, it's still around on youtube and on the Stanford Engineering Everywhere site. The coursera version of the class is much more introductory and skips significant parts of the full Stanford version. link to this video link

valgazeon Hacker News • Recommends

On “Baidu’s Andrew Ng on Deep Learning and Innovation in Silicon Valley”

Watch Ng at 57 minutes discuss the "cocktail party" problem and present an amazing solution: link to this video

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 and link to this video and my favorite channels are Standford and MIT open courseware link and link

joaogui1on Hacker News • Recommends

On “Ask HN: Full-on machine learning for 2020, what are the best resources?”

Machine Learning: * link to this video : Andrew Ng's machine Learning course, the recommended entry point by most people * link : More kaggle focused, but also more modern and has interesting projects Do both courses simultaneously, take good notes, write useful flashcards, and above all do all the exercises and projects Deep Learning * link - Very hands-on, begin with " Practical Deep Learning for Coders" and then "Advanced Deep Learning for coders" * link : More bottom-up approach, helps to understand the theory better Do those two courses in parallel (you can try 2 weeks of coursera followed by one of fastai in the beginning, and then just alternate between them), take notes, write good flashc…

u/SSchlesingerin r/compsci • Mentions

On “Entertaining CS YouTube channels?”

…trical-engineering-and-computer-science/6-851-advanced-data-structures-spring-2012/ Finally, Dave Ackley just has great things to say about artificial life and computing in general, and he puts out videos that are really just cool, not mentally demanding at all. If you think that the other ones are too academic this one is totally not like that. link to this video Oh, and another great course is by Andrew Ng on machine learning. This one is pretty mathy but will teach you a lot if you stick it out. link to this video…

u/uibertoin r/compsci • Mentions

On “Can I learn machine learning on my own time?”

Decent, free, soon-to-be-published book as a pdf: link Andrew Ng (Stanford) published videos of his ML course: link

wcsunon Hacker News • Mentions

On “Ask HN: How do I start with Machine Learning?”

First, finish lectures by Professor Gilbert Strang. link To my memory, session notes of CS229 is good enough for understanding SVM and gaussian distributions. Also watch youtube videos. link link to this video If you just want to use the libraries, you can stop here. If you want to know more, read chapters 1-3 of nonlinear programming by Professor Dimitri Bertsekas before convex optimization. link Then, you can try to finish EE364 and watch the videos. link link If you want to roll your own algorithms, you have to know some optimization tools. link And there is some statistics knowledge you have to fill in. I used these: link link R is used in the courses.

ahalanon Hacker News • Mentions

On “58,000 Sign Up for Stanford AI Course”

I bet ~5% will complete the course here is the number of views for Andrew Ng's Intro to machine learning course on youtube ( link to this video ) lec 1, 206612 lec 2, 91234 lec 3, 49000 lec 4, 36823 lec 5, 27782 lec 6, 26347 lec 7, 22075 lec 8, 20713 lec 9, 15665 lec 10, 14142 lec 11, 16573 lec 12, 14296 lec 13, 12401 lec 14, 15022 lec 15, 12290 lec 16, 10760 lec 17, 8986 lec 18, 13639 lec 19, 10219 lec 20, 11373

visargaon Hacker News • Mentions

On “Ask HN: Top Coursera Courses?”

I hope everyone was referring to Andrew's blackboard course: link to this video instead of the one that started Coursera: link