Daily Learning

转载自 Machine Learning Courses
Tips : 网上有很多好的资源, 例如转载的这篇Blog中列举的这些, 但是真正掌握知识都是一样的, 选择合适自己的一门课上懂就可以.

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Courses

• Courses on machine learning
  http://homepages.inf.ed.ac.uk/rbf/IAPR/researchers/MLPAGES/mlcourses.htm

• CSC2535 – Spring 2013 Advanced Machine Learning
  instructor: by Hinton, University of Toronto
  homepage: http://www.cs.toronto.edu/~hinton/csc2535/

• Stanford CME 323: Distributed Algorithms and Optimization
  http://stanford.edu/~rezab/dao/

• University at Buffalo CSE574: Machine Learning and Probabilistic Graphical Models Course
  http://www.cedar.buffalo.edu/~srihari/CSE574/

• Stanford CS229: Machine Learning Autumn 2015
  instructor: Andrew Ng
  homepage: http://cs229.stanford.edu/
  project page: http://cs229.stanford.edu/projects2015.html

• Stanford / Winter 2014-2015 CS229T/STATS231: Statistical Learning Theory
  instructor: Percy Liang
  homepage: http://web.stanford.edu/class/cs229t/
  lecture notes: http://web.stanford.edu/class/cs229t/notes.pdf

• CMU Fall 2015 10-715: Advanced Introduction to Machine Learning
  instructor: Alex Smola, Barnabas Poczos
  homepage: http://www.cs.cmu.edu/~bapoczos/Classes/ML10715_2015Fall/
  video: http://pan.baidu.com/s/1qWvcsUS

• 2015 Machine Learning Summer School: Convex Optimization Short Course
  instructor: S. Boyd and S. Diamond
  Lecture slides and IPython notebooks: https://stanford.edu/~boyd/papers/cvx_short_course.html

• STA 4273H (Winter 2015): Large Scale Machine Learning
  http://www.cs.toronto.edu/~rsalakhu/STA4273_2015/
  University of Oxford: Machine Learning: 2014-2015
  homepage: https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/
  course materials: https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/
  lectures: http://pan.baidu.com/s/1bndbxJh#path=%252FDeep%2520Learning%2520Lectures
  github: https://github.com/oxford-cs-ml-2015/

• Computer Science 294: Practical Machine Learning (Fall 2009)
  instructor: Michael Jordan
  homepage: https://www.cs.berkeley.edu/~jordan/courses/294-fall09/

• Statistics, Probability and Machine Learning Short Course
  homepage: http://www-staff.it.uts.edu.au/~ydxu/statistics.htm
  youku: http://i.youku.com/u/UMzIzNDgxNTg5Ng
  youbube: https://www.youtube.com/playlist?list=PLFze15KrfxbF0n1zTNoFIaDpxnSyfgNgc

• Statistical Learning
  https://lagunita.stanford.edu/courses/HumanitiesScience/StatLearning/Winter2014/about

• Machine learning courses online
  http://fastml.com/machine-learning-courses-online/

• Build Intelligent Applications: Master machine learning fundamentals in five hands-on courses (Coursera)
  https://www.coursera.org/specializations/machine-learning

• Machine Learning
  http://www.cs.ubc.ca/~nando/540-2013/lectures.html

• Princeton Computer Science 598D: Overcoming Intractability in Machine Learning
  http://www.cs.princeton.edu/courses/archive/spring15/cos598D/

• Princeton Computer Science 511: Theoretical Machine Learning
  instructor: Rob Schapire
  homepage: http://www.cs.princeton.edu/courses/archive/spring14/cos511/schedule.html

• MACHINE LEARNING FOR MUSICIANS AND ARTISTS
  https://www.kadenze.com/courses/machine-learning-for-musicians-and-artists/info

• CMSC 726: Machine Learning
  homepage: http://www.cbcb.umd.edu/~hcorrada/PML/index.html

• MIT: 9.520: Statistical Learning Theory and Applications, Fall 2015
  http://www.mit.edu/~9.520/fall15/

• CMU: Machine Learning: 10-701/15-781, Spring 2011
  instructor: Tom Mitchell
  homepage: http://www.cs.cmu.edu/~tom/10701_sp11/
  lectures: http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml

• NLA 2015 course material
  ipn: http://nbviewer.jupyter.org/github/Bihaqo/nla2015/blob/master/table_of_contents.ipynb

• CS 189/289A: Introduction to Machine Learning(with videos)
  homepage: http://www.cs.berkeley.edu/~jrs/189/

• An Introduction to Statistical Machine Learning Spring 2014 (for ACM Class)
  http://bcmi.sjtu.edu.cn/log/courses/ml_2014_spring_acm.html

• CS 159: Advanced Topics in Machine Learning (Spring 2016)
  intro: Online Learning, Multi-Armed Bandits, Active Learning, Human-in-the-Loop Learning, Reinforcement Learning
  instructor: Yisong Yue
  homepage: http://www.yisongyue.com/courses/cs159/

• Advanced Statistical Computing (Vanderbilt University)
  intro: Course covers numerical optimization, Markov Chain Monte Carlo (MCMC), Metropolis-Hastings, Gibbs sampling, estimation-maximization (EM) algorithms, data augmentation algorithms with applications for model fitting and techniques for dealing with missing data
  homepage: http://stronginference.com/Bios8366/
  lecture: http://stronginference.com/Bios8366/lectures.html
  github: https://github.com/fonnesbeck/Bios8366

• Stanford CS229: Machine Learning Spring 2016
  instructor: John Duchi
  homepage: http://cs229.stanford.edu/
  materials: http://cs229.stanford.edu/materials.html

• Machine Learning: 2015-2016
  homepage: https://www.cs.ox.ac.uk/teaching/courses/2015-2016/ml/
  materials: http://www.cs.ox.ac.uk/people/varun.kanade/teaching/ML-HT2016/index.html

• CS273a: Introduction to Machine Learning
  homepage: http://sli.ics.uci.edu/Classes/2015W-273a
  youtube: https://www.youtube.com/playlist?list=PLaXDtXvwY-oDvedS3f4HW0b4KxqpJ_imw
  course notes: http://sli.ics.uci.edu/Classes-CS178-Notes/Classes-CS178-Notes

• Machine Learning CS-433
  homepage: http://mlo.epfl.ch/page-136795.html
  github: https://github.com/epfml/ML_course

Machine Learning on Distributed System

• Distributed Machine Learning with Apache Spark
  edx: https://prod-edx-mktg-edit.edx.org/course/distributed-machine-learning-apache-uc-berkeleyx-cs120x

PhD-level Courses (with video lectures)

• Phd-level courses
  reddit: https://www.reddit.com/r/MachineLearning/comments/51qhc8/phdlevel_courses/

• Advanced Introduction to Machine Learning
  homepage: http://www.cs.cmu.edu/~bapoczos/Classes/ML10715_2015Fall/index.html
  video: https://www.youtube.com/playlist?list=PL4DwY1suLMkcu-wytRDbvBNmx57CdQ2pJ&jct=q4qVgISGxJql7TlE6eSLKa8Wwci8SA

• STA 4273H (Winter 2015): Large Scale Machine Learning
  http://www.cs.toronto.edu/~rsalakhu/STA4273_2015/

• Statistical Learning Theory and Applications (MIT)
  homepage: http://www.mit.edu/~9.520/fall15/index.html
  video: https://www.youtube.com/playlist?list=PLyGKBDfnk-iDj3FBd0Avr_dLbrU8VG73O

• (REGML 2016) Regularization Methods for Machine Learning
  homepage: http://lcsl.mit.edu/courses/regml/regml2016/
  video: https://www.youtube.com/playlist?list=PLbF0BXX_6CPJ20Gf_KbLFnPWjFTvvRwCO

• Convex Optimization: Spring 2015
  homepage: http://www.stat.cmu.edu/~ryantibs/convexopt-S15/
  video: https://www.youtube.com/playlist?list=PLjbUi5mgii6BZBhJ9nW7eydgycyCOYeZ6

• CMU: Probabilistic Graphical Models (10-708, Spring 2014)
  instructor: Eric Xing
  homepage: http://www.cs.cmu.edu/~epxing/Class/10708/
  lecture: http://www.cs.cmu.edu/~epxing/Class/10708-14/lecture.html

• Advanced Optimization and Randomized Methods
  instructor: A. Smola, S. Sra
  homepage: http://www.cs.cmu.edu/~suvrit/teach/index.html

• Machine Learning for Robotics and Computer Vision
  homepage: http://vision.in.tum.de/teaching/ws2013/ml_ws13
  video: https://www.youtube.com/watch?v=QZmZFeZxEKI&list=PLTBdjV_4f-EIiongKlS9OKrBEp8QR47Wl

• Statistical Machine Learning
  homepage: http://www.stat.cmu.edu/~larry/=sml/
  video: https://www.youtube.com/playlist?list=PLTB9VQq8WiaCBK2XrtYn5t9uuPdsNm7YE
  mirror: http://pan.baidu.com/s/1eSuJ1Nc

PhD-level Courses (without video lectures)

• Probabilistic Graphical Models (10-708, Spring 2016)
  http://www.cs.cmu.edu/~epxing/Class/10708-16/lecture.html

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Resources

Learn Machine learning online – List of machine learning courses available online

blog: http://bafflednerd.com/learn-machine-learning-online/
awesomeMLmath

intro: Curated list to learn the math basics for machine learning. Note that this is a biased list from a Deep Learning research.
github: https://github.com/EderSantana/awesomeMLmath
MOOCs for Machine Learning

https://medium.com/@amarbudhiraja/moocs-for-machine-learning-5a2f2c6cdcfe#.1m2v38e0y