資源|機器學(xué)習(xí)/深度學(xué)習(xí)線上開放課程集錦
寫在最前面
本文整理了機器學(xué)習(xí)/深度學(xué)習(xí)比較優(yōu)秀的線上開放課程，主要為了方便小伙伴們個人學(xué)習(xí)所用，目前機器學(xué)習(xí)與深度學(xué)習(xí)發(fā)展迅速，新的課程也層出不窮，所以本帖也會不定期更新，包括更新課程網(wǎng)址以及添加新的好課程。所以，各位小伙伴有比較好的課程一定要在評論區(qū)留言，我看到后會將其更新上來，以分享給其它小伙伴。也歡迎留下你的贊！
注意這里對各個課程并沒有做好與壞的評論，一般來說，入門機器學(xué)習(xí)的經(jīng)典課程是Stanford: CS229，入門深度學(xué)習(xí)的經(jīng)典課程是Stanford: CS231n。
1
Table of Contents
Deep Learning
Machine Learning
Reinforcement Learning
Computer Vision
Artificial Intelligence
2
Deep Learning
[CMU: 11-785 Introduction to Deep Learning](http://deeplearning.cs.cmu.edu/) [Spring 2018] [DL]
[Stanford: CS230 Deep Learning](https://web.stanford.edu/class/cs230/) [Winter 2018][DL] [[Ng中文筆記-黃海廣](http://www.ai-start.com/)]
[University of Chicago: CMSC 35246 Deep Learning ](http://ttic.uchicago.edu/~shubhendu/Pages/CMSC35246.html) [Spring 2017][DL]
[Stanford: CS231n Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/) [Spring 2017][CV] [[中文翻譯](http://www.mooc.ai/course/268#modal)]
[Stanford: CS224n Natural Language Processing with Deep Learning](http://web.stanford.edu/class/cs224n/) [Winter 2018][NLP]
[Stanford: CS 20 Tensorflow for Deep Learning Research](http://web.stanford.edu/class/cs20si/) [Winter 2018][TensorFlow]
[Stanford: Theories of Deep Learning (STATS 385)](https://stats385.github.io/) [Fall 2017][DL]
[CMU: 10707 Deep Learning](http://www.cs.cmu.edu/~rsalakhu/10707/) [Fall 2017][DL]
[National Taiwan University: Applied Deep Learning /Machine Learning and Having It Deep and Structured](https://www.csie.ntu.edu.tw/~yvchen/f106-adl/) [2017 Fall][DL] [[Hung-yi Lee](http://speech.ee.ntu.edu.tw/~tlkagk/index.html)]
[Theano: Deep Learning Tutorials](http://deeplearning.net/tutorial/) [Theano]
[Mxnet: Deep Learning-The Straight Dope](http://gluon.mxnet.io/) [2017][Mxnet] [[中文](http://zh.gluon.ai/)]
[MIT: 6.S191 Introduction to Deep Learning](http://introtodeeplearning.com/) [2018][DL]
[UVA: DEEP LEARNING COURSE](http://uvadlc.github.io/) [DL]
[Fast.ai: Practical Deep Learning For Coders](http://course.fast.ai/) [2018][DL]
[CMU: CS 11-747 Neural networks fro NLP](http://phontron.com/class/nn4nlp2018/#) [Spring 2018][NLP]
[Stanford: CS224S / LINGUIST285 - Spoken Language Processing](http://web.stanford.edu/class/cs224s/) [Spring 2017][Speech Recognition]
[Berkeley: CS 294-131: Special Topics in Deep Learning](https://berkeley-deep-learning.github.io/cs294-131-f17/) [Fall 2017][Advanced DL]
[CMU: 16-385 Computer Vision](http://www.cs.cmu.edu/~16385/) [Spring 2018][CV]
[Columbia University: E6894 Deep Learning for Computer Vision, Speech, and Language](http://llcao.net/cu-deeplearning17/schedule.html) [Spring 2017][DL]
[Colorado: CSCI 5922 Neural Networks and Deep Learning](https://www.cs.colorado.edu/~mozer/Teaching/syllabi/DeepLearningFall2017/) [Fall 2017][DL]
[UIUC: CS 598 LAZ Cutting-Edge Trends in Deep Learning and Recognition](http://slazebni.cs.illinois.edu/spring17/) [2017][DL]
[UPC: Deep Learning for Speech and Language](https://telecombcn-dl.github.io/2017-dlsl/) [2017 Winter][Speech Recognition]
[toronto: CSC 321 Intro to Neural Networks and Machine Learning](http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/) [CSC 321 Winter 2018][DL]
3
Computer Vision
1.[toronto: CSC420: Intro to Image Understanding](http://www.teach.cs.toronto.edu/~csc420h/fall/) [Fall 2017][CV]
4
Machine Learning
[Stanford: CS229 Machine Learning](http://cs229.stanford.edu/) [Autumn 2017][ML]
[University of Notre Dame: Statistical Computing for Scientists and Engineers](https://www.zabaras.com/statisticalcomputing) [Fall 2017][SL]
[CMU: Statistical Machine Learning](http://www.stat.cmu.edu/~ryantibs/statml/) [Spring 2017][ML]
[Carnegie Mellon University:10-701/15-781 Machine Learning](http://www.cs.cmu.edu/~tom/10701_sp11/) [Spring 2011][ML]
[toronto: CSC411? introduction to Machine Learning](http://www.cs.toronto.edu/~jlucas/teaching/csc411/) [Fall 2017][ML]
[MIT: 6.S099 Artificial General Intelligence](https://agi.mit.edu/) [2018]
[MIT 6.S094: Deep Learning for Self-Driving Cars](https://selfdrivingcars.mit.edu/) [2018]
5
Reinforcement Learning
[Berkeley: CS 294 Deep Reinforcement Learning](http://rll.berkeley.edu/deeprlcourse/?utm_source=qq&utm_medium=social) [Fall 2017][RL]
[CMU: 10703 Deep RL and Control](http://www.cs.cmu.edu/~rsalakhu/10703/) [Fall 2018][RL]
[Stanford: CS234: Reinforcement Learning](http://web.stanford.edu/class/cs234/index.html?utm_source=wechat_session&utm_medium=social) [Winter 2018][RL]
參考
深度學(xué)習(xí)名校課程大全:?https://zhuanlan.zhihu.com/p/33580103
Awesome Deep Learning:?https://github.com/xiaohu2015/awesome-deep-learning
機器學(xué)習(xí)算法全棧工程師
? ? ? ? ? ? ? ? ? ? ? ? ? ? 一個用心的公眾號
進群，學(xué)習(xí)，得幫助
你的關(guān)注，我們的熱度，
我們一定給你學(xué)習(xí)最大的幫助