机器学习资源

| 研究学术  | 机器学习基础 

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 ScienceWhat 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][CourseraCS229


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


打赏作者


上一篇:神经网络简介     下一篇:机器学习实战技能