“机器学习是近20多年兴起的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。机器学习理论主要是设计和分析一些让 可以自动“ 学习”的算法。机器学习算法是一类从数据中自动分析获得规律,并利用规律对未知数据进行预测的算法。因为学习算法中涉及了大量的统计学理论,机器学习与统计推断学联系尤为密切,也被称为统计学习理论。算法设计方面,机器学习理论关注可以实现的,行之有效的学习算法。很多 推论问题属于 无程序可循难度,所以部分的机器学习研究是开发容易处理的近似算法。” ——中文维基百科

知识荟萃

机器学习课程 专知搜集

  1. cs229 机器学习 吴恩达
  2. 台大 李宏毅 机器学习
  3. 爱丁堡大学 机器学习与模式识别
  4. Courses on machine learning
  5. CSC2535 -- Spring 2013 Advanced Machine Learning
  6. Stanford CME 323: Distributed Algorithms and Optimization
  7. University at Buffalo CSE574: Machine Learning and Probabilistic Graphical Models Course
  8. Stanford CS229: Machine Learning Autumn 2015
  9. Stanford / Winter 2014-2015 CS229T/STATS231: Statistical Learning Theory
  10. CMU Fall 2015 10-715: Advanced Introduction to Machine Learning
  11. 2015 Machine Learning Summer School: Convex Optimization Short Course
  12. STA 4273H [Winter 2015]: Large Scale Machine Learning
  13. University of Oxford: Machine Learning: 2014-2015
  14. Computer Science 294: Practical Machine Learning [Fall 2009]
  1. Statistics, Probability and Machine Learning Short Course
  2. Statistical Learning
  3. Machine learning courses online
  4. Build Intelligent Applications: Master machine learning fundamentals in five hands-on courses
  5. Machine Learning
  6. Princeton Computer Science 598D: Overcoming Intractability in Machine Learning
  7. Princeton Computer Science 511: Theoretical Machine Learning
  8. MACHINE LEARNING FOR MUSICIANS AND ARTISTS
  9. CMSC 726: Machine Learning
  10. MIT: 9.520: Statistical Learning Theory and Applications, Fall 2015
  11. CMU: Machine Learning: 10-701/15-781, Spring 2011
  12. NLA 2015 course material
  13. CS 189/289A: Introduction to Machine Learning[with videos]
  14. An Introduction to Statistical Machine Learning Spring 2014 [for ACM Class]
  15. CS 159: Advanced Topics in Machine Learning [Spring 2016]
  16. Advanced Statistical Computing [Vanderbilt University]
  17. Stanford CS229: Machine Learning Spring 2016
  18. Machine Learning: 2015-2016
  19. CS273a: Introduction to Machine Learning
  20. Machine Learning CS-433
  21. Machine Learning Introduction: A machine learning course using Python, Jupyter Notebooks, and OpenML
  22. Advanced Introduction to Machine Learning
  23. STA 4273H [Winter 2015]: Large Scale Machine Learning
  24. Statistical Learning Theory and Applications [MIT]
  25. Regularization Methods for Machine Learning
  1. Convex Optimization: Spring 2015
  2. CMU: Probabilistic Graphical Models [10-708, Spring 2014]
  3. Advanced Optimization and Randomized Methods
  4. Machine Learning for Robotics and Computer Vision
  5. Statistical Machine Learning
  6. Probabilistic Graphical Models [10-708, Spring 2016]

数学基础

Calculus

  1. Khan Academy Calculus [https://www.khanacademy.org/math/calculus-home]

Linear Algebra

  1. Khan Academy Linear Algebra
  2. Linear Algebra MIT 目前最好的线性代数课程

Statistics and probability

  1. edx Introduction to Statistics [https://www.edx.org/course/introduction-statistics-descriptive-uc-berkeleyx-stat2-1x]
  2. edx Probability [https://www.edx.org/course/introduction-statistics-probability-uc-berkeleyx-stat2-2x]
  3. An exploration of Random Processes for Engineers [http://www.ifp.illinois.edu/~hajek/Papers/randomprocDec11.pdf]
  4. Information Theory [http://colah.github.io/posts/2015-09-Visual-Information/]

VIP内容

这项工作的目标是向读者介绍加权有限状态自动机及其在机器学习中的应用。我首先介绍了机器学习中自动机的使用,然后介绍了受体、换能器和它们的相关属性。然后详细描述了加权自动机的许多核心运算。在此基础上,通过解释自动分化及其在加权自动机中的应用,进一步向研究前沿迈进。最后一节介绍几个扩展示例,以加深对加权自动机及其操作的熟悉,以及它们在机器学习中的使用。

https://awnihannun.com/writing/automata_ml.html

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最新论文

The ability to optimize multiple competing objective functions with high sample efficiency is imperative in many applied problems across science and industry. Multi-objective Bayesian optimization (BO) achieves strong empirical performance on such problems, but even with recent methodological advances, it has been restricted to simple, low-dimensional domains. Most existing BO methods exhibit poor performance on search spaces with more than a few dozen parameters. In this work we propose MORBO, a method for multi-objective Bayesian optimization over high-dimensional search spaces. MORBO performs local Bayesian optimization within multiple trust regions simultaneously, allowing it to explore and identify diverse solutions even when the objective functions are difficult to model globally. We show that MORBO significantly advances the state-of-the-art in sample-efficiency for several high-dimensional synthetic and real-world multi-objective problems, including a vehicle design problem with 222 parameters, demonstrating that MORBO is a practical approach for challenging and important problems that were previously out of reach for BO methods.

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