斯坦福大学资源

斯坦福大学网站：https://cs.stanford.edu/courses/schedules/2017-2018.autumn.php

Course	Title	Instructor	Time	Room
cs1C	Introduction to Computing at Stanford	Smith	by arrangement
cs1U	Practical Unix	Zelenski/Sarka	TTh 1:30-2:50	STLC 104
cs7	Personal Finance for Engineers	Nash	T 4:30-5:50	200-034
cs9	Problem-solving for the CS Technical Interview	Cain/Lee	T 3:00-4:50	STLC 111
cs28	AI, Entrepreneurship & Society in 21st Cntry & Bey	Ganguli/Taneja	M 4:30-5:50	HerrinT175
cs45N	Computers and Photography: From Capture to Sharing	Garcia-Molina	MW 2:30-4:20	Gates 505
cs50	Using Tech for Good	Cain	MWF 12:30-1:20	STLC115
cs56N	Great Discoveries and Inventions in Computing	Hennessy	TTh 9:00-10:20	STLC118
cs102	Big Data: Tools & Techniques, Discoveries & Pitfal	Widom	TTh 1:30-2:50	320-105
cs103	Mathematical Foundations of Computing	Schwarz	MWF 3:00-4:20	Nvidia Aud
cs103A	Mathematical Problem-solving Strategies	Schwarz	T 3:00-5:50	STLC115
cs105	Introduction to Computers	Young	MWF 1:30-2:20	HerrinT175
cs106A	Programming Methodology	Sahami	MWF 1:30-2:20	Hewlett200/201
cs106AJ	Programming Methodology in JavaScript	Cain	MWF 10:30-11:20	300-300
cs106B	Programming Abstractions	Lee	MWF 12:30-1:20	Nvidia Aud
cs106X	Programming Abstractions (Accelerated)	Stepp	MWF 12:30-1:20	420-041
cs107	Computer Organization and Systems	Zelenski/Gregg	MF 1:30-2:50	CubberleyAud
cs108	Object-Oriented Systems Design	Young	MW 3:00-4:20	530-127
cs109	Intro to Probability for Computer Scientists	Piech	MWF 3:30-4:20	Hewlett200
cs110	Principles of Computer Systems	Cain	MWF 1:30-2:50	Skilling Aud
cs131	Computer Vision: Foundations and Applications	Niebles Duque/	TTh 1:30-2:50	200-002
cs142	Web Applications	Rosenblum	MWF 10:30-11:20	200-002
cs144	Introduction to Computer Networking	Levis/McKeown	MW 3:00-4:20	Skilling Aud
cs145	Introduction to Databases	Bailis	TTh 3:00-4:20	Nvidia Aud
cs146	Introduction to Game Design and Development	James/Riedel-K	TTh 4:30-5:50	380-380C
cs147	Introduction to Human-Computer Interaction Design	Landay	MW 11:30-1:20	Hewlett 201
cs148	Introduction to Computer Graphics and Imaging	Fedkiw	TTh 12:00-1:20	Nvidia Aud
cs154	Introduction to Automata and Complexity Theory	Reingold	TTh 10:30-11:50	Skilling Aud
cs157	Logic and Automated Reasoning	Genesereth	TTh 12:00-1:20	Gates B01
cs161	Design and Analysis of Algorithms	Wootters	MW 1:30-2:50	370-370
cs183E	Effective Leadership in High-tech	Finley/Goldfei	W 4:30-5:50	300-303
cs191	Senior Project	(none listed)	by arrangement
cs191W	Writing Intensive Senior Project	(none listed)	by arrangement
cs192	Programming Service Project	(none listed)	by arrangement
cs193P	iOS Application Development	Hegarty	MW 4:30-5:50	Hewlett200
cs198	Teaching Computer Science	Sahami/Conklin	M 4:30-6:20	370-370
cs198B	Additional Topics in Teaching Computer Science	Sahami/Conklin	TTh 4:30-5:20	MitchB67
cs199	Independent Work	(none listed)	by arrangement
cs199P	Independent Work	(none listed)	by arrangement
cs202	Law for Computer Science Professionals	Hansen	Th 4:30-5:50	Lathrop 299
cs206	Exploring Computational Journalism	Hamilton/Agraw	T 1:30-3:20	JSK Fell Garage
cs208E	Great Ideas in Computer Science	Gregg	TTh 1:30-2:50	160-319
cs221	Artificial Intelligence: Principles & Techniques	Liang/Ermon	MW 1:30-2:50	Nvidia Aud
cs224W	Analysis of Networks	Leskovec	TTh 1:30-2:50	Nvidia Aud
cs229	Machine Learning	Ng/Boneh	MW 9:30-10:50	Nvidia Aud
cs230	Deep Learning	Ng/Katanforoos	M 11:30-12:50	Hewlett 102
cs238	Decision Making under Uncertainty	Kochenderfer	MW 1:30-2:50	GatesB01
cs241	Embedded Systems Workshop	Levis/Horowitz	MW 10:30-12:20	HerrinT185
cs242	Programming Languages	Crichton	MW 4:30-5:50	Skilling Aud
cs244B	Distributed Systems	Mazieres	MW 3:00-4:20	Thornton 102
cs265	Randomized Algorithms and Probabilistic Analysis	Valiant	TTh 10:30-11:50	STLC115
cs273B	Deep Learning in Genomics and Biomedicine	Kundaje/Zou	MW 3:00-4:20	Hewlett201
cs274	Reps and Algor for Computational Molecular Bio	Altman	TTh 4:30-5:50	Gates B01
cs279	Comp Biology: Struct & Org of Biomolecules & Cells	Dror	TTh 3:00-4:20	Shriram104
cs300	Departmental Lecture Series	Ousterhout	MW 4:30-5:50	370-370
cs309A	Cloud Computing Seminar	Chou	T 4:30-5:50	Skilling Aud
cs315B	Parallel Computing Research Project	Aiken	TTh 3:00-4:20	200-219
cs325B	Data for Sustainable Development	Ermon/Lobell	T 1:30-4:20	Shriram 108
cs326	Topics in Advanced Robotic Manipulation	Bohg	TTh 10:30-11:50	Education 207
cs331B	Representation Learning in Computer Vision	Savarese/Zahir	M 1:30-4:20	Campbell 126
cs332	Advanced Survey of Reinforcement Learning	Brunskill	MW 1:30-2:50	HerrinT195
cs333	Safe and Interactive Robotics	Sadigh	TTh 3:00-4:20	McMurtry 360
cs348C	Computer Graphics: Animation and Simulation	James	TTh 1:30-2:50	GatesB12
cs349D	Cloud Computing Technology	Kozyrakis/Zaha	MW 10:30-12:20	380-380W
cs375	Large-Scale Neural Net Modeling for Neuroscience	Yamins	MW 4:30-5:50 PM	Lathrop299
cs376	Human-Computer Interaction Research	Bernstein	MW 3:00-4:20	Littlefield107
cs390A	Curricular Practical Training	(none listed)	by arrangement
cs390B	Curricular Practical Training	(none listed)	by arrangement
cs390C	Curricular Practical Training	(none listed)	by arrangement
cs390P	Part-time Curricular Practical Training	(none listed)	by arrangement
cs393	Computer Laboratory	(none listed)	by arrangement
cs395	Independent Database Project	(none listed)	by arrangement
cs399	Independent Project	(none listed)	by arrangement
cs399P	Independent Project	(none listed)	by arrangement
cs428	Computation and Cognition: Probabilistic Approach	Goodman	TTh 1:30-2:50 PM	200-305
cs448B	Data Visualization	Agrawala	MW 4:30-5:50 PM	Lathrop 282
cs476A	Music, Computing and Design I	Wang	MW 3:30-5:20	Knoll217
cs499	Advanced Reading and Research	(none listed)	by arrangement
cs499P	Advanced Reading and Research	(none listed)	by arrangement
cs522	Seminar in Artificial Intelligence in Healthcare	Dror	Th 4:30-5:20	Hewlett200
cs53SI	Discussion in Tech for Good	Sahami	T 4:30-6:20pm	200-107
cs544	Mobile Computing Seminar	James/Riedel-K	T 4:30-5:50	420-041
cs547	Human-Computer Interaction Seminar	Bernstein	F 12:30-2:20	Gates B01
cs581	Media Innovation	Grimes	T 12:00-1:20	Gates 176
cs801	TGR Project	(none listed)	by arrangement
cs802	TGR Dissertation	(none listed)	by arrangement

机器学习（Machine Learning，简称 ML）和计算机视觉（Computer Vision，简称 CV）是非常令人着迷、非常酷炫、颇具挑战性同时也是涉及面很广的领域。本文整理了机器学习和计算机视觉的相关学习资源，目的是帮助许多和我一样希望深刻理解“智能”背后原理的人，用最为高效的方式学习最为前沿的技术和知识。

另外请见我后一篇博客里列的数据挖掘的学习资源。

 

wikipedia.org，历史，领域概述，资源链接：

Machine learning，介绍了ML所处理的问题、常用算法、应用、软件等，右侧列举了细分条目；

List of machine learning concepts，Category:Machine learning，列举出了更多ML相关概念和条目；

Computer vision，同样，介绍了CV所处理的问题、常用方法、应用等，底部列举了细分条目；

List of computer vision topics，Category:Computer vision，列举了更多CV相关条目。

 

大学课程、在线教程：

Stanford 关于ML和CV计算机课程（按推荐排序）：

1、Andrew NG机器学习课程网易公开课：http://open.163.com/special/opencourse/machinelearning.html

2、机器学习课程教学官网： http://cs229.stanford.edu/syllabus.html

3、Coursera最新版：https://www.coursera.org/learn/machine-learning/

cs229 Machine Learning，

cs229T Statistical Learning Theory，

cs231N Convolutional Neural Networks for Visual Recognition，

cs231A Computer Vision:From 3D Recontruct to Recognition，

cs231B The Cutting Edge of Computer Vision，

cs221 Artificial Intelligence: Principles & Techniques，

cs131 Computer Vision: Foundations and Applications，

cs369L A Theoretical Perspective on Machine Learning，

cs205A Mathematical Methods for Robotics, Vision & Graph，

cs231MMobile Computer Vision，

这些课程大都可以下载PPT，更多课程请见Courses | Stanford Computer Science，Open class room的ML课程Machine Learning，Unsupervised Feature Learning and Deep Learning，Coursera的ML课程：Machine Learning，以及Stanford在线教程Deep learning tuorial；

更多大学课程可以用“machine learning course”或“computer vision course”为关键字搜索，这里是Google的国内镜像，这样就不需要FanQiang了。

 

专著、书籍：

ML：

机器学习，周志华，2016；

统计学习方法，李航，2012；

Deep Learning: Methods and Applications, Li Deng and Dong Yu, 2014；

Introduction to Machine Learning (3rd ed.), Ethem Alpaydin, 2014；

Machine Learning: An Algorithmic Perspective (2nd ed.), Stephen Marsland, 2015；

Deep Learning，一本在线书籍；

Neural Networks and Learning Machines (3rd ed.), Simon O. Haykin, 2008；有中文译本：神经网络与机器学习；

Pattern Recognition and Machine Learning, Christopher Bishop, 2006；有中文译本：模式识别与机器学习；

Machine Learning: a Probabilistic Perspective, Kevin P. Murphy, 2012；

CV：

Concise Computer Vision: An Introduction into Theory and Algorithms, Klette, Reinhard, 2014；

Computer Vision: Algorithms and Applications, Szeliski, Richard, 2011；有中文译本：计算机视觉——算法与应用；

Multiple View Geometry in Computer Vision (2nd ed.), Richard Hartley and Andrew Zisserman, 2004；

An Invitation to 3-D Vision: From Images to Geometric Models,  Yi Ma, Stefano Soatto, Jana Kosecka, S. Shankar Sastry, 2004；

Robot vision, Berthold K. P. Horn, 1986；有中文译本：机器视觉；

Image Processing, Analysis, and Machine Vision (3rd ed.), Milan Sonka, Vaclav Hlavac, Roger Boyle, 2007；有中文译本：图像处理、分析与机器视觉；

推荐一个非常好的搜索英文电子书的网站：Library Genesis。

 

学术论文：

ML、CV领域的顶级期刊：TPAMI，IJCV，学术会议：ACL，CVPR，ICML，ICCV，NIPS，ECCV，ACCV等；

CVPapers 对CV领域学术论文做了很好的整理；

ImageNet 每年举办的图像识别比赛很能代表CV最高水平，MS COCO是类似比赛，KITTI上有很多数据以及CV算法的排名，这里是一个数据集的列表，这里是CV数据集；

arXiv.org，很多最新论文首先发表在这里；

当然还是推荐Google Scholar，这里是一个镜像网站。

 

学习网站：

deeplearning.net：一个非常好的机器学习网站，有dataset、software、reading list连接；

VisionBib.Com：学术大牛整理的CV资源；

CVonline有一个非常全面的资源链接；

新智元和机器之心是很好的机器学习资讯平台，另外推荐一些微信公众号：机器学习研究会，程序媛的日常。

 

程序、库：

OpenCV：一个C++视觉库，使用广泛；

Torch, Theano：两个很强大的支持CUDA显卡加速的Python机器学习库；

Caffe：很多研究者使用的Deep Learning库；

R语言：一个方便开发机器学习程序的环境；