斯坦福大学网站：https://cs.stanford.edu/courses/schedules/2017-2018.autumn.php
Course | Title | Instructor | Time | Room |
Introduction to Computing at Stanford | Smith | by arrangement | ||
Practical Unix | Zelenski/Sarka | TTh 1:30-2:50 | STLC 104 | |
Personal Finance for Engineers | Nash | T 4:30-5:50 | 200-034 | |
Problem-solving for the CS Technical Interview | Cain/Lee | T 3:00-4:50 | STLC 111 | |
AI, Entrepreneurship & Society in 21st Cntry & Bey | Ganguli/Taneja | M 4:30-5:50 | HerrinT175 | |
Computers and Photography: From Capture to Sharing | Garcia-Molina | MW 2:30-4:20 | Gates 505 | |
Using Tech for Good | Cain | MWF 12:30-1:20 | STLC115 | |
Great Discoveries and Inventions in Computing | Hennessy | TTh 9:00-10:20 | STLC118 | |
Big Data: Tools & Techniques, Discoveries & Pitfal | Widom | TTh 1:30-2:50 | 320-105 | |
Mathematical Foundations of Computing | Schwarz | MWF 3:00-4:20 | Nvidia Aud | |
Mathematical Problem-solving Strategies | Schwarz | T 3:00-5:50 | STLC115 | |
Introduction to Computers | Young | MWF 1:30-2:20 | HerrinT175 | |
Programming Methodology | Sahami | MWF 1:30-2:20 | Hewlett200/201 | |
Programming Methodology in JavaScript | Cain | MWF 10:30-11:20 | 300-300 | |
Programming Abstractions | Lee | MWF 12:30-1:20 | Nvidia Aud | |
Programming Abstractions (Accelerated) | Stepp | MWF 12:30-1:20 | 420-041 | |
Computer Organization and Systems | Zelenski/Gregg | MF 1:30-2:50 | CubberleyAud | |
Object-Oriented Systems Design | Young | MW 3:00-4:20 | 530-127 | |
Intro to Probability for Computer Scientists | Piech | MWF 3:30-4:20 | Hewlett200 | |
Principles of Computer Systems | Cain | MWF 1:30-2:50 | Skilling Aud | |
Computer Vision: Foundations and Applications | Niebles Duque/ | TTh 1:30-2:50 | 200-002 | |
Web Applications | Rosenblum | MWF 10:30-11:20 | 200-002 | |
Introduction to Computer Networking | Levis/McKeown | MW 3:00-4:20 | Skilling Aud | |
Introduction to Databases | Bailis | TTh 3:00-4:20 | Nvidia Aud | |
Introduction to Game Design and Development | James/Riedel-K | TTh 4:30-5:50 | 380-380C | |
Introduction to Human-Computer Interaction Design | Landay | MW 11:30-1:20 | Hewlett 201 | |
Introduction to Computer Graphics and Imaging | Fedkiw | TTh 12:00-1:20 | Nvidia Aud | |
Introduction to Automata and Complexity Theory | Reingold | TTh 10:30-11:50 | Skilling Aud | |
Logic and Automated Reasoning | Genesereth | TTh 12:00-1:20 | Gates B01 | |
Design and Analysis of Algorithms | Wootters | MW 1:30-2:50 | 370-370 | |
Effective Leadership in High-tech | Finley/Goldfei | W 4:30-5:50 | 300-303 | |
Senior Project | (none listed) | by arrangement | ||
Writing Intensive Senior Project | (none listed) | by arrangement | ||
Programming Service Project | (none listed) | by arrangement | ||
iOS Application Development | Hegarty | MW 4:30-5:50 | Hewlett200 | |
Teaching Computer Science | Sahami/Conklin | M 4:30-6:20 | 370-370 | |
Additional Topics in Teaching Computer Science | Sahami/Conklin | TTh 4:30-5:20 | MitchB67 | |
Independent Work | (none listed) | by arrangement | ||
Independent Work | (none listed) | by arrangement | ||
Law for Computer Science Professionals | Hansen | Th 4:30-5:50 | Lathrop 299 | |
Exploring Computational Journalism | Hamilton/Agraw | T 1:30-3:20 | JSK Fell Garage | |
Great Ideas in Computer Science | Gregg | TTh 1:30-2:50 | 160-319 | |
Artificial Intelligence: Principles & Techniques | Liang/Ermon | MW 1:30-2:50 | Nvidia Aud | |
Analysis of Networks | Leskovec | TTh 1:30-2:50 | Nvidia Aud | |
Machine Learning | Ng/Boneh | MW 9:30-10:50 | Nvidia Aud | |
Deep Learning | Ng/Katanforoos | M 11:30-12:50 | Hewlett 102 | |
Decision Making under Uncertainty | Kochenderfer | MW 1:30-2:50 | GatesB01 | |
Embedded Systems Workshop | Levis/Horowitz | MW 10:30-12:20 | HerrinT185 | |
Programming Languages | Crichton | MW 4:30-5:50 | Skilling Aud | |
Distributed Systems | Mazieres | MW 3:00-4:20 | Thornton 102 | |
Randomized Algorithms and Probabilistic Analysis | Valiant | TTh 10:30-11:50 | STLC115 | |
Deep Learning in Genomics and Biomedicine | Kundaje/Zou | MW 3:00-4:20 | Hewlett201 | |
Reps and Algor for Computational Molecular Bio | Altman | TTh 4:30-5:50 | Gates B01 | |
Comp Biology: Struct & Org of Biomolecules & Cells | Dror | TTh 3:00-4:20 | Shriram104 | |
Departmental Lecture Series | Ousterhout | MW 4:30-5:50 | 370-370 | |
Cloud Computing Seminar | Chou | T 4:30-5:50 | Skilling Aud | |
Parallel Computing Research Project | Aiken | TTh 3:00-4:20 | 200-219 | |
Data for Sustainable Development | Ermon/Lobell | T 1:30-4:20 | Shriram 108 | |
Topics in Advanced Robotic Manipulation | Bohg | TTh 10:30-11:50 | Education 207 | |
Representation Learning in Computer Vision | Savarese/Zahir | M 1:30-4:20 | Campbell 126 | |
Advanced Survey of Reinforcement Learning | Brunskill | MW 1:30-2:50 | HerrinT195 | |
Safe and Interactive Robotics | Sadigh | TTh 3:00-4:20 | McMurtry 360 | |
Computer Graphics: Animation and Simulation | James | TTh 1:30-2:50 | GatesB12 | |
Cloud Computing Technology | Kozyrakis/Zaha | MW 10:30-12:20 | 380-380W | |
Large-Scale Neural Net Modeling for Neuroscience | Yamins | MW 4:30-5:50 PM | Lathrop299 | |
Human-Computer Interaction Research | Bernstein | MW 3:00-4:20 | Littlefield107 | |
Curricular Practical Training | (none listed) | by arrangement | ||
Curricular Practical Training | (none listed) | by arrangement | ||
Curricular Practical Training | (none listed) | by arrangement | ||
Part-time Curricular Practical Training | (none listed) | by arrangement | ||
Computer Laboratory | (none listed) | by arrangement | ||
Independent Database Project | (none listed) | by arrangement | ||
Independent Project | (none listed) | by arrangement | ||
Independent Project | (none listed) | by arrangement | ||
Computation and Cognition: Probabilistic Approach | Goodman | TTh 1:30-2:50 PM | 200-305 | |
Data Visualization | Agrawala | MW 4:30-5:50 PM | Lathrop 282 | |
Music, Computing and Design I | Wang | MW 3:30-5:20 | Knoll217 | |
Advanced Reading and Research | (none listed) | by arrangement | ||
Advanced Reading and Research | (none listed) | by arrangement | ||
Seminar in Artificial Intelligence in Healthcare | Dror | Th 4:30-5:20 | Hewlett200 | |
Discussion in Tech for Good | Sahami | T 4:30-6:20pm | 200-107 | |
Mobile Computing Seminar | James/Riedel-K | T 4:30-5:50 | 420-041 | |
Human-Computer Interaction Seminar | Bernstein | F 12:30-2:20 | Gates B01 | |
Media Innovation | Grimes | T 12:00-1:20 | Gates 176 | |
TGR Project | (none listed) | by arrangement | ||
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/
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，
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；