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 LearningUnsupervised 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;


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