机器学习资源

入门资料

1. Hacker’s guide to Neural Networks［Link］
2. Probabilistic Programming & Bayesian Methods for Hackers［Link］
3. Visualizing MNIST: An Exploration of Dimensionality Reduction［Link］
4. Data Science in Python［Link］
5. Bayesian network与python概率编程实战入门［Link］

Deep Learning

1. DEEP LEARNING（An MIT Press book in preparation）［Link］
2. Neural Networks and Deep Learning［Link］
3. Unsupervised Feature Learning and Deep Learning［Link］
4. TUTORIAL ON DEEP LEARNING FOR VISION［Link］
5. Quoc Le’s Lectures on Deep Learning［Link（视频）］
6. Deep Learning Master Class［Link（视频）］
7. 深度学习进阶线路图［Link］
8. 深度神经网络DNN的多GPU数据并行框架及其在语音识别的应用［Link］
9. 解密接近人脑的智能学习机器——深度学习及并行化实现［Link］
10. 看DeepMind如何用Reinforcement learning玩游戏［Link］

资源整合

1. Some of useful machine learning resources from beginner to intermediate［Link］
2. My deep learning reading list［Link］
3. Where to Learn Deep Learning – Courses, Tutorials, Software［Link］
4. Deep Learning – important resources for learning and understanding［Link］
5. A curated list of awesome Machine Learning frameworks, libraries and software［Link］
6. 个人阅读的Deep Learning方向的paper整理［Link］

竞赛与数据

1. kaggle：The Home of Data Science［What are some alternatives to Kaggle?］
2. DRIVENDATA：Data science competitions to save the world
3. 百度：百度开放研究社区
4. 阿里巴巴：天池大数据科研平台
5. 360：360开放实验室
6. 卖数据的：数据堂
7. machine learning data set repository：mldata
8. machine learning open source software：mloss

竞赛经验谈

1. CIKM Competition数据挖掘竞赛夺冠算法-陈运文
2. Kaggle Competition Past Solutions
3. Winning solution of Kaggle Higgs competition: what a single model can do?

Demos & Codes

1. Toronto Deep Learning Demos
2. ConvNetJS Deep Q Learning Demo
3. 100 Best GitHub: Deep Learning
4. cuda-convnet2
5. Deep Visual-Semantic Alignments for Generating Image Descriptions

视频课程

####Machine Learning［Stanford University］［Coursera、CS229］

Instructors：Andrew Ng

####Learning From Data［California Institute of Technology］

Instructors：Yaser S. Abu-Mostafa

####機器學習基石 (Machine Learning Foundations)［國立台灣大學］［Coursera］

Instructors：Hsuan-Tien Lin（林軒田）

####機器學習技法 (Machine Learning Techniques)［國立台灣大學］［Coursera］

Instructors：Hsuan-Tien Lin（林軒田）

####Introduction to Machine Learning［Carnegie Mellon University］［土豆2013］［Youtube2015］

Instructors：Alex Smola
参考教材：Introduction to Machine Learning［下载］

####Machine Learning［Carnegie Mellon University］

Instructors：Tom Mitchell

####Machine Learning［Cornell University］

Instructors：Thorsten Joachims

####Machine Learning［University of Washington］［Coursera］

Instructors：Pedro Domingos
预览可看视频

####Big Data, Large Scale Machine Learning［New York University］［Video］

Instructors：John Langford and Yann LeCun

####Probabilistic Graphical Models［Carnegie Mellon University］

Instructors：Eric Xing

####Probabilistic Graphical Models［Stanford University］［Coursera］

Instructors：Daphne Koller

####Neural Networks for Machine Learning［University of Toronto］［Coursera］

Instructors：Geoffrey Hinton

####Discrete Inference and Learning in Artificial Vision［École Centrale Paris］［Coursera］

Instructors：Nikos Paragios and Pawan Kumar

####Intro to Machine Learning: Pattern Recognition for Fun and Profit［Stanford University］［Udacity］

Instructors：Sebastian Thrun

####Machine Learning［Georgia Institute of Technology］［Udacity］［Supervised Learning, Unsupervised Learning, Reinforcement Learning］

Instructors：Charles Isbell and Michael Littman

####Statistical Learning［Stanford University］

Instructors：Trevor Hastie and Rob Tibshirani
参考教材：An Introduction to Statistical Learning, with Applications in R［下载］

####机器学习导论［上海交通大学］／统计机器学习［上海交通大学］

Instructors：张志华

####Introduction to Recommender Systems［University of Minnesota］［Coursera］

Instructors：Joseph A. Konstan & Michael D. Ekstrand

####计算广告学［网易云课堂］

Instructors：刘鹏

####机器学习［龙星计划2012］

Instructors：余凯 & 张潼

####信息处理和人工智能的深度学习［龙星计划2013］

Instructors：邓力

####Large Scale Machine Learning［University of Toronto］

Instructors：Russ Salakhutdinov

參考教材：

• Christopher M. Bishop (2006) Pattern Recognition and Machine Learning, Springer.
• Machine Learning: A Probabilistic Perspective, by Kevin P. Murphy.
• Trevor Hastie, Robert Tibshirani, Jerome Friedman (2009) The Elements of Statistical Learning
• David MacKay (2003) Information Theory, Inference, and Learning Algorithms

####Artificial Intelligence［University of California, Berkeley］［Edx］

Instructors：Dan Klein & Pieter Abbeel

####Statistical Machine Learning［Carnegie Mellon University］

Instructors：Ryan Tibshirani & Larry Wasserman

####Deep Learning［New York University］

Instructors：Yann LeCun

####Machine Learning［The University of British Columbia (Youtube)］［University of Oxford］

Instructors：Nando de Freitas