Wednesday, November 30, 2016

Free Machine Learning/ Deep Learning Moocs



List of Free Machine Learning MOOCS [Updated: June 13 2020]

Mathematics:
 http://people.duke.edu/~das76/Mathematics%20for%20Political%20and%20Social%20Research%20Syllabus_Siegel.pdf

Guide to deep learning: http://yerevann.com/a-guide-to-deep-learning/

Deep Learning: http://ofir.io/How-to-Start-Learning-Deep-Learning/

Awesome Deep Learning: https://github.com/ChristosChristofidis/awesome-deep-learning

Hugo Larochelle's Neural network class: http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html

Berkeley Special topics in deep learning: https://berkeley-deep-learning.github.io/cs294-131-s17/

Coursera Deep learning: https://www.coursera.org/specializations/deep-learning



Udacity:


https://www.udacity.com/course/intro-to-machine-learning--ud120

https://www.udacity.com/course/machine-learning--ud262


Many useful resources: http://aimedicines.com/2017/03/17/all-ai-resources-at-one-place/

Nanodegree: https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009


Machine Learning Specialization https://www.coursera.org/specializations/machine-learning


Neural Networks for Machine Learning https://www.coursera.org/learn/neural-networks



Practical Machine Learning https://www.coursera.org/learn/practical-machine-learning


Applied Machine Learning in Python https://www.coursera.org/learn/python-machine-learning


Machine Learning With Big Data https://www.coursera.org/learn/big-data-machine-learning


Machine Learning https://www.edx.org/co…/machine-learning-columbiax-csmm-102x


Principles of Machine Learning https://www.edx.org/…/principles-machine-learning-microsoft…


Applied Machine Learning https://www.edx.org/…/applied-machine-learning-microsoft-da…


Learning From Data (Introductory Machine Learning) https://www.edx.org/…/learning-data-introductory-machine-ca…


Machine Learning for Data Science and Analytics https://www.edx.org/…/machine-learning-data-science-analyti…

Machine Learning: University of UTAH: http://www.cs.utah.edu/~piyush/teaching/cs5350.html#schedule


Artificial Intelligence https://www.edx.org/micro…/columbiax-artificial-intelligence


Artificial Intelligence (AI) https://www.edx.org/…/artificial-intelligence-ai-columbiax-…

6.S094: Deep Learning for Self-Driving Cars (MIT ) http://selfdrivingcars.mit.edu/

CS 294: Deep Reinforcement Learning, Spring 2017 (Berkeley) http://rll.berkeley.edu/deeprlcourse/

CS224d: Deep Learning for Natural Language Processing(Stanford) http://cs224d.stanford.edu/

Practical Deep Learning for coders: http://course.fast.ai/about.html

Cornell Machine Learning: http://www.cs.cornell.edu/courses/cs4780/2015fa/page4/index.html

NYU : http://cilvr.cs.nyu.edu/doku.php?id=courses:start

MIT Introduction to deep learning: http://introtodeeplearning.com/index.html

Stanford: Tensorflow for Deep Learning Research: http://web.stanford.edu/class/cs20si/index.html

Joyce Ho's Machine Learning 2017: http://joyceho.github.io/cs534_s17/index.html

Stanford Stats 202: Data Mining and Analysis: http://web.stanford.edu/class/stats202/content/lectures.html

Statistical Machine Learning CMU: http://www.stat.cmu.edu/~ryantibs/statml/

Tom Mitchell ML CMU: http://www.cs.cmu.edu/~ninamf/courses/601sp15/lectures.shtml

Learning deep learning with keras (blog post) http://p.migdal.pl/2017/04/30/teaching-deep-learning.html

Deep learning and reinforcement learning summer school 2016: https://mila.umontreal.ca/en/cours/deep-learning-summer-school-2017/slides/

Deep learning and reinforcement learning summer school 2017: http://videolectures.net/deeplearning2017_montreal/


Deep learning: Pytorch  https://fleuret.org/dlc/

Deep Learning SAP : https://open.sap.com/courses/ml2

Understanding Maching Learning (Uni of Waterloo) : https://www.youtube.com/playlist?list=PLFze15KrfxbH8SE4FgOHpMSY1h5HiRLMm

Applications of Deep Neural Networks for Tensorflow and Keras(Washington University): https://www.youtube.com/playlist?list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN


NLP :

Harvard NLP: http://cs287.fas.harvard.edu/#schedule
Stanford NLP: https://github.com/stanfordnlp/cs224n-winter17-notes
http://web.stanford.edu/class/cs224n/index.html)
Oxford NLP: https://github.com/oxford-cs-deepnlp-2017/lectures
Stanford NLP: Deep Learning for Natural Language Processing(Without Magic)
https://nlp.stanford.edu/courses/NAACL2013/
Stanford NLP with deep learning (2017): https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6
Coursera Stanford NLP: http://web.stanford.edu/~jurafsky/NLPCourseraSlides.html
EMNLP 2017 videos https://ku.cloud.panopto.eu/Panopto/Pages/Sessions/List.aspx


Shujian Liu's recommendations :https://www.linkedin.com/feed/update/urn:li:activity:6340023717552283648

IPAM 2012 videos: http://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-deep-learning-feature-learning/?tab=schedule

9.520/6.860: Statistical Learning Theory and Applications, Fall 2017 (MIT): http://www.mit.edu/~9.520/fall17/#psets

Steven Skiena Data Science book/videos : http://www3.cs.stonybrook.edu/~skiena/data-manual/lectures/


2018


1) Introduction to Deep Learning (CMU) 2018: http://deeplearning.cs.cmu.edu/

2) Applied Machine Learning Spring 2018: http://www.cs.columbia.edu/~amueller/comsw4995s18/schedule/

3) MIT 6.S094: Deep Learning for Self-Driving Cars: https://selfdrivingcars.mit.edu/

4) MIT 6.S099: Artificial General Intelligence : https://agi.mit.edu/

5) CS 20 Stanford: Tensorflow for Deep Learning Research: http://web.stanford.edu/class/cs20si/syllabus.html

6) 6.S191: Introduction to Deep Learning MIT: http://introtodeeplearning.com/

7) FAST AI part 1 (2018 ): http://course.fast.ai/

8) EE-559 Deep Learning ( Pytorch): https://documents.epfl.ch/users/f/fl/fleuret/www/dlc/

9) New Deep Learning Techniques (IPAM Workshop 2018) : http://www.ipam.ucla.edu/programs/workshops/new-deep-learning-techniques/?tab=schedule

10) Deep Learning for Visual Computing (Pytorch 2018, IIT KGP ): https://www.youtube.com/channel/UC0fYFk8BaNEH4VMIlClrf8w/videos

11) CS230 Stanford Deep Learning (similar to coursera materials): https://cs230-stanford.github.io/

12) Google Machine Learning Crash Course: https://developers.google.com/machine-learning/crash-course/


13) CS 234: Reinforcement Learning: http://web.stanford.edu/class/cs234/schedule.html

14) CS 224N: NLP with deep learning(Stanford): http://web.stanford.edu/class/cs224n/syllabus.html

15) ScaledML 2018: https://www.matroid.com/blog/post/slides-and-videos-from-scaledml-2018

16) CMU : Neural Network for NLP(2018): http://www.phontron.com/class/nn4nlp2018/schedule.html

17) AM207 Harvard: https://am207.github.io/2018spring/lectures/

18) Machine Learning for Natural Language https://harvard-ml-courses.github.io/cs287-web/

19) Software Engineering for Data Scientists(UW) http://uwseds.github.io/

20) Hardware Accelerator for Machine Learning (Stanford): https://cs217.github.io/ 

21) Foundations of Machine Learning(Bloomberg): https://bloomberg.github.io/foml/#home

22) DeepBayes 2018: http://deepbayes.ru/#materials

23) REGML (MIT) : http://lcsl.mit.edu/courses/regml/regml2018/

24) Deep Learning and Reinforcement learning summer school: http://videolectures.net/DLRLsummerschool2018_toronto/

25) Yandex NLP course: https://github.com/yandexdataschool/nlp_course

26 Berkeley Special Topics in Deep Learning: https://berkeley-deep-learning.github.io/cs294-131-f18/


2019:

1) Dive into Deep Learning:  https://d2l.ai/index.html

2) Vistalab Technion Deep Learning: https://vistalab-technion.github.io/cs236605/lectures/

3) Salesforce Explore Deep Learning for NLP: https://trailhead.salesforce.com/en/content/learn/trails/explore-deep-learning-for-nlp

4) CMU Neural Networks for NLP: http://phontron.com/class/nn4nlp2019/

5) CS224n: Natural Language Processing with Deep Learning | Winter 2019: https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z

6) CS230 Deep Learning (Stanford Andrew Ng): http://cs230.stanford.edu/

7) Full-stack deep learning (Abbeel & team): https://fullstackdeeplearning.com/march2019#

8) Deep Learning, ULi├Ęge, Spring 2019:  https://github.com/glouppe/info8010-deep-learning

9) Scaled ML https://www.youtube.com/watch?v=XJ5imIWlnQo&list=PLRM2gQVaW_wWXoUnSfZTxpgDmNaAS1RtG

10) Matrix Methods in Data Analysis, Signal Processing, and Machine Learning https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/

11) Machine Learning for Intelligent Systems (Cornell): http://www.cs.cornell.edu/courses/cs4780/2018fa/page18/index.html

12) Applied Machine Learning( CS Columbia): https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/

13) Introduction to Machine Learning (Korbit AI): https://www.korbit.ai/

14) Ancient secrets of computer vision : https://pjreddie.com/courses/computer-vision/

15) Machine Learning for Intelligent Systems (Cornell): http://www.cs.cornell.edu/courses/cs4780/2018fa/

16) NPTEL Deep learning: https://www.youtube.com/playlist?list=PLyqSpQzTE6M9gCgajvQbc68Hk_JKGBAYT

2020:

1) Deep Unsupervised Learning (Berkeley): https://sites.google.com/view/berkeley-cs294-158-sp20/home

2) Many resources: https://deep-learning-drizzle.github.io/

3) Applied Machine Learning: https://www.cs.columbia.edu/~amueller/comsw4995s20/

4) Deep Learning (DeepMind) https://www.youtube.com/playlist?list=PLqYmG7hTraZCDxZ44o4p3N5Anz3lLRVZF

5) Advanced Deep Learning for Computer Vision (DVLG, Germany): https://dvl.in.tum.de/teaching/adl4cv-ss20/

6) Deep Learning, FB Yann LeCun https://atcold.github.io/pytorch-Deep-Learning/




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