Machine-learning-Related

机器学习(Machine Learning, ML)是一门多领域交叉学科，涉及概率论、统计学、逼近论、凸分析、算法复杂度理论等多门学科。专门研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技能，重新组织已有的知识结构使之不断改善自身的性能。 它是人工智能的核心，是使计算机具有智能的根本途径，其应用遍及人工智能的各个领域，它主要使用归纳、综合而不是演绎。

1. Ebook
2. Courses
3. 会议
4. OverViews
5. License \& Copyright

Ebook

• Patterns, Predictions, and Actions A story about machine learning.Moritz Hardt and Benjamin Recht pdf
• The Deep Learning [An MIT Press book - Ian Goodfellow and Yoshua Bengio and Aaron Courville] textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free.
• 深度学习-Ian Goodfellow（伊恩·古德费洛）著.pdf
  • Author：Ian Goodfellow and Yoshua Bengio and Aaron Courville： https://www.deeplearningbook.org/
  • 链接: https://pan.baidu.com/s/1OTz-ah0XV1nrq_3tf8RtEA 提取码: e75m

• Reinforcement Learning: An Introduction

• The Journal of Machine Learning Research (JMLR), established in 2000, provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online.
• Neural Networks and Deep Learning is a free online book. The book will teach you about: 1. Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data; 2. Deep learning, a powerful set of techniques for learning in neural networks .Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neural networks and deep learning.
• Tutorials on topics in machine learning
• ICML(International Conference on Machine Learning) is the leading international machine learning conference and is supported by the International Machine Learning Society (IMLS).

• Computational Complexity: A Modern Approach

• Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises: Printer-Friendly PDF Version

Courses

• CS229: Machine Learning
• CSS 490 / 590 - Introduction to Machine Learning
• CMPUT 609: Reinforcement Learning for Artificial Intelligence
• CS240h: Functional Systems in Haskell

• Introduction to Statistical Learning

• Graduate Summer School: Deep Learning, Feature Learning
  • Institute for Pure & Applied Mathematics - An NSF Math Institute at UCLA
• Machine Learning Surveys: A list of literature surveys, reviews, and tutorials on Machine Learning and related topics
• Machine Learning Lab: Frank Hutter as Hector Endowed Fellow at the ELLIS Institute Tübingen

会议

OverViews

• 爱可可老师分享 https://www.yuque.com/fly51fly/gvl1ma

  License & Copyright

• 版权声明：Copyright © 2019-2024 要庆生. All rights reserved. 未经本人同意请勿转载。经本人同意后转载时请注明出处。
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• 免责声明：此项目仅供学习交流使用，严禁用于商业用途及其他非法目的，请在24小时之内删除！如有侵权，请联系删除。
• 业余时间所作，难免有不足及错漏之处，敬请包涵指正，可通过github仓库在线留言或告知；如需补充其他相关专业信息，亦可邮件通知或github仓库在线留言；同时欢迎各位热心人士star、fork或共同参与维护仓库

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