Artificial Intelligence Links

8/17/2019 Artificial Intelligence Links

1/4

Artificial Intelligence

This is going to be a list of resources for learning the required topics to be considered

knowledgeable in the field of artificial intelligence. This will be everything I can find,

including textbooks, researchers, papers, courses, video series, and notes. Some of thesebooks can be put in other categories, but I just put them in what I would see most.

Basics

Programming

● Learning how to program

- https://www.edx.org/course/introduction-computer-science-harvardx-cs50x

● Python

- https://developers.google.com/edu/python/

- http://learnpythonthehardway.org/

- https://www.coursera.org/specializations/python ● R

- https://www.coursera.org/learn/r-programming

Mathematics

● http://www.amazon.com/Introduction-Algorithms-3rd-Edition-Press/dp/0262033844

● http://inst.eecs.berkeley.edu/~cs70/sp16/

● https://www.coursera.org/learn/calculus1

● https://www.coursera.org/learn/advanced-calculus

● https://www.coursera.org/course/matrix

● https://www.edx.org/course/effective-thinking-through-mathematics-utaustinx-ut-9

-1x

● https://www.khanacademy.org/math/linear-algebra

● http://www.amazon.com/Applied-Linear-Algebra-Lorenzo-Sadun/dp/0821844415

Statistics

● https://www.edx.org/course/introduction-probability-science-mitx-6-041x-1

● http://www.amazon.com/Introduction-Probability-Edition-Dimitri-Bertsekas/dp/188

652923X

Data Science

● https://www.coursera.org/specializations/jhu-data-science

● https://courses.edx.org/courses/BerkeleyX/CS100.1x/1T2015/fbe63aa3c95948e391

2fa128aedec27d/

● https://lagunita.stanford.edu/courses/Engineering/db/2014_1/about

● https://www.coursera.org/learn/intro-to-big-data

http://www.amazon.com/Introduction-Probability-Edition-Dimitri-Bertsekas/dp/188652923Xhttp://www.amazon.com/Introduction-Probability-Edition-Dimitri-Bertsekas/dp/188652923Xhttps://www.khanacademy.org/math/linear-algebrahttps://www.khanacademy.org/math/linear-algebrahttps://www.edx.org/course/effective-thinking-through-mathematics-utaustinx-ut-9-01xhttps://www.edx.org/course/effective-thinking-through-mathematics-utaustinx-ut-9-01xhttps://www.coursera.org/learn/advanced-calculushttps://www.coursera.org/learn/advanced-calculushttps://www.coursera.org/learn/calculus1https://www.coursera.org/learn/calculus1http://inst.eecs.berkeley.edu/~cs70/sp16/http://inst.eecs.berkeley.edu/~cs70/sp16/https://www.coursera.org/specializations/pythonhttp://learnpythonthehardway.org/https://developers.google.com/edu/python/https://www.edx.org/course/introduction-computer-science-harvardx-cs50xhttps://www.edx.org/course/introduction-computer-science-harvardx-cs50xhttps://www.coursera.org/learn/intro-to-big-datahttps://lagunita.stanford.edu/courses/Engineering/db/2014_1/abouthttps://courses.edx.org/courses/BerkeleyX/CS100.1x/1T2015/fbe63aa3c95948e3912fa128aedec27d/https://courses.edx.org/courses/BerkeleyX/CS100.1x/1T2015/fbe63aa3c95948e3912fa128aedec27d/https://www.coursera.org/specializations/jhu-data-sciencehttp://www.amazon.com/Introduction-Probability-Edition-Dimitri-Bertsekas/dp/188652923Xhttp://www.amazon.com/Introduction-Probability-Edition-Dimitri-Bertsekas/dp/188652923Xhttps://www.edx.org/course/introduction-probability-science-mitx-6-041x-1http://www.amazon.com/Applied-Linear-Algebra-Lorenzo-Sadun/dp/0821844415https://www.khanacademy.org/math/linear-algebrahttps://www.edx.org/course/effective-thinking-through-mathematics-utaustinx-ut-9-01xhttps://www.edx.org/course/effective-thinking-through-mathematics-utaustinx-ut-9-01xhttps://www.coursera.org/course/matrixhttps://www.coursera.org/learn/advanced-calculushttps://www.coursera.org/learn/calculus1http://inst.eecs.berkeley.edu/~cs70/sp16/http://www.amazon.com/Introduction-Algorithms-3rd-Edition-Press/dp/0262033844https://www.coursera.org/learn/r-programminghttps://www.coursera.org/specializations/pythonhttp://learnpythonthehardway.org/https://developers.google.com/edu/python/https://www.edx.org/course/introduction-computer-science-harvardx-cs50x


2/4

● https://www.coursera.org/course/patterndiscovery

● https://www.coursera.org/course/algs4partI

● https://www.coursera.org/course/algs4partII

Machine Learning

● https://www.coursera.org/course/neuralnets ● https://www.youtube.com/watch?v=UzxYlbK2c7E

● http://videolectures.net/mackay_course_01/

● http://rll.berkeley.edu/deeprlcourse/

● https://www.coursera.org/course/pgm

● http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html

● http://rll.berkeley.edu/deeprlcourse/docs/ng-thesis.pdf

● http://statweb.stanford.edu/~tibs/ElemStatLearn/

● https://courses.edx.org/courses/BerkeleyX/CS190.1x/1T2015/info

● https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter201

6/about ● http://www.cs.ubc.ca/~murphyk/MLbook/index.html

● https://www.coursera.org/learn/practical-machine-learning

● http://archive.ics.uci.edu/ml/

● https://www.coursera.org/specializations/machine-learning

Deep Learning

● http://cilvr.nyu.edu/doku.php?id=deeplearning:slides:start

● http://www.deeplearningbook.org/

● https://www.udacity.com/course/deep-learning--ud730

● https://sites.google.com/site/deeplearningsummerschool/home ● http://cs224d.stanford.edu/

●

Cognitive Thinking

● http://www.amazon.com/Fundamentals-Cognitive-Psychology-Ronald-Kellogg/dp/1

483347583/ref=pd_sim_14_2?ie=UTF8&dpID=51HGdwbnU0L&dpSrc=sims&preST=_

AC_UL160_SR129%2C160_&refRID=0W1X5MSBH6YEYVKS75QZ

● http://www.amazon.com/Constructing-Language-Usage-Based-Theory-Acquisition/d

p/0674017641

● http://www.amazon.com/Action-Perception-Representation-Mind-Alva/dp/0262640

635

● http://www.amazon.com/The-Vision-Revolution-Overturns-Everything/dp/19352517

67

● http://www.amazon.com/On-Intelligence-Jeff-Hawkins/dp/0805074562

● http://mind.sourceforge.net/theory5.html

Neuroscience

http://mind.sourceforge.net/theory5.htmlhttp://www.amazon.com/On-Intelligence-Jeff-Hawkins/dp/0805074562http://www.amazon.com/The-Vision-Revolution-Overturns-Everything/dp/1935251767http://www.amazon.com/The-Vision-Revolution-Overturns-Everything/dp/1935251767http://www.amazon.com/Action-Perception-Representation-Mind-Alva/dp/0262640635http://www.amazon.com/Action-Perception-Representation-Mind-Alva/dp/0262640635http://www.amazon.com/Constructing-Language-Usage-Based-Theory-Acquisition/dp/0674017641http://www.amazon.com/Constructing-Language-Usage-Based-Theory-Acquisition/dp/0674017641http://www.amazon.com/Fundamentals-Cognitive-Psychology-Ronald-Kellogg/dp/1483347583/ref=pd_sim_14_2?ie=UTF8&dpID=51HGdwbnU0L&dpSrc=sims&preST=_AC_UL160_SR129%2C160_&refRID=0W1X5MSBH6YEYVKS75QZhttp://www.amazon.com/Fundamentals-Cognitive-Psychology-Ronald-Kellogg/dp/1483347583/ref=pd_sim_14_2?ie=UTF8&dpID=51HGdwbnU0L&dpSrc=sims&preST=_AC_UL160_SR129%2C160_&refRID=0W1X5MSBH6YEYVKS75QZhttp://www.amazon.com/Fundamentals-Cognitive-Psychology-Ronald-Kellogg/dp/1483347583/ref=pd_sim_14_2?ie=UTF8&dpID=51HGdwbnU0L&dpSrc=sims&preST=_AC_UL160_SR129%2C160_&refRID=0W1X5MSBH6YEYVKS75QZhttp://cs224d.stanford.edu/https://sites.google.com/site/deeplearningsummerschool/homehttps://www.udacity.com/course/deep-learning--ud730http://www.deeplearningbook.org/http://cilvr.nyu.edu/doku.php?id=deeplearning:slides:starthttps://www.coursera.org/specializations/machine-learninghttp://archive.ics.uci.edu/ml/https://www.coursera.org/learn/practical-machine-learninghttp://www.cs.ubc.ca/~murphyk/MLbook/index.htmlhttps://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/abouthttps://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/abouthttps://courses.edx.org/courses/BerkeleyX/CS190.1x/1T2015/infohttp://statweb.stanford.edu/~tibs/ElemStatLearn/http://rll.berkeley.edu/deeprlcourse/docs/ng-thesis.pdfhttp://webdocs.cs.ualberta.ca/~sutton/book/the-book.htmlhttps://www.coursera.org/course/pgmhttp://rll.berkeley.edu/deeprlcourse/http://videolectures.net/mackay_course_01/https://www.youtube.com/watch?v=UzxYlbK2c7Ehttps://www.coursera.org/course/neuralnetshttps://www.coursera.org/course/algs4partIIhttps://www.coursera.org/course/algs4partIhttps://www.coursera.org/course/patterndiscovery


3/4

● http://www.amazon.com/Neuroscience-Exploring-Mark-F-Bear/dp/0781778174/ref=

pd_sim_14_3?ie=UTF8&dpID=51JUiv62mEL&dpSrc=sims&preST=_AC_UL160_SR124

%2C160_&refRID=1NGD47SA7VJWJTTD9WFM

● http://www.amazon.com/Developmental-Cognitive-Neuroscience-Mark-Johnson/dp/

1444330853

Artificial Intelligence

● https://www.edx.org/course/artificial-intelligence-uc-berkeleyx-cs188-1x

● http://ai.neocities.org/AiSteps.html

● https://www.udacity.com/course/intro-to-artificial-intelligence--cs271

● http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artif

icial-intelligence-fall-2010/

Researchers and People to know

● https://en.wikipedia.org/wiki/Andrew_Ng

● https://en.wikipedia.org/wiki/Geoffrey_Hinton ● https://en.wikipedia.org/wiki/Nick_Bostrom

Textbooks/Papers

● Bayesian Reasoning and Machine Learning - David Barber

● Where Do Features Come From? Geoffrey Hinton

● Modeling Documents With a Deep Boltzmann Machine - Geoffrey Hinton, Nitish

Srivastava, and Ruslan Salakhutdinov

● Distilling the Knowledge in a Neural Network - Geoffrey Hinton, Oriol Vinyalis, and

Jeff Dean

● Grammar as a Foreign Language - Hinton plus others● Information Science and Statistics - Christopher Bishop

● Information Theory, Inference, and Learning Algorithms - David MacKay

● An Introduction into Statistical Learning with Applications in R

● Dropout: A Simple Way to Prevent Neural Networks from Overfitting - Toronto CS

● Machine Learning - Peter Flach

● Building Machine Learning Systems with Python - Willi Richert

● To Recognize Shapes, First Learn to Generate Images - Hinton

● Deep Learning - LeCun, Bengio, Hinton

● A Fast Learning Algorithm for Deep Belief Nets - Hinton, Teh, Osindero

● Speech Recognition with Deep Recurrent Neural Networks - Hinton, Mohammed,

Graves

● Reducing the Dimensionality of Data with Neural Networks - Hinton, Salakhuditinov

● Superintelligence Paths Dangers Stragies - Bostrom

Other

● https://wiki.python.org/moin/PythonForArtificialIntelligence

● https://www.tensorflow.org/

● http://frnsys.com/ai_notes/

http://frnsys.com/ai_notes/https://www.tensorflow.org/https://wiki.python.org/moin/PythonForArtificialIntelligencehttps://en.wikipedia.org/wiki/Nick_Bostromhttps://en.wikipedia.org/wiki/Geoffrey_Hintonhttps://en.wikipedia.org/wiki/Andrew_Nghttp://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/https://www.udacity.com/course/intro-to-artificial-intelligence--cs271http://ai.neocities.org/AiSteps.htmlhttps://www.edx.org/course/artificial-intelligence-uc-berkeleyx-cs188-1xhttp://www.amazon.com/Developmental-Cognitive-Neuroscience-Mark-Johnson/dp/1444330853http://www.amazon.com/Developmental-Cognitive-Neuroscience-Mark-Johnson/dp/1444330853http://www.amazon.com/Neuroscience-Exploring-Mark-F-Bear/dp/0781778174/ref=pd_sim_14_3?ie=UTF8&dpID=51JUiv62mEL&dpSrc=sims&preST=_AC_UL160_SR124%2C160_&refRID=1NGD47SA7VJWJTTD9WFMhttp://www.amazon.com/Neuroscience-Exploring-Mark-F-Bear/dp/0781778174/ref=pd_sim_14_3?ie=UTF8&dpID=51JUiv62mEL&dpSrc=sims&preST=_AC_UL160_SR124%2C160_&refRID=1NGD47SA7VJWJTTD9WFMhttp://www.amazon.com/Neuroscience-Exploring-Mark-F-Bear/dp/0781778174/ref=pd_sim_14_3?ie=UTF8&dpID=51JUiv62mEL&dpSrc=sims&preST=_AC_UL160_SR124%2C160_&refRID=1NGD47SA7VJWJTTD9WFM


4/4

● https://books.google.com/books?uid=111815788291054011027&as_coll=1012&sour

ce=gbs_lp_bookshelf_list (HUGE AMOUNT OF TEXTBOOKS)

References

● http://wp.goertzel.org/agi-curriculum/

● https://docs.google.com/spreadsheets/d/1NSbURoynPVnOvSCtmaIX6zV8wl6n3ybacnNGMyb-v-0/edit#gid=0

● Wojciech Zaremba

https://www.reddit.com/user/mkdir_not_war

https://www.reddit.com/user/don_chow

https://www.reddit.com/user/AiHasBeenSolved
https://www.reddit.com/user/AiHasBeenSolvedhttps://www.reddit.com/user/don_chowhttps://www.reddit.com/user/mkdir_not_warhttps://docs.google.com/spreadsheets/d/1NSbURoynPVnOvSCtmaIX6zV8wl6n3ybacnNGMyb-v-0/edit#gid=0https://docs.google.com/spreadsheets/d/1NSbURoynPVnOvSCtmaIX6zV8wl6n3ybacnNGMyb-v-0/edit#gid=0http://wp.goertzel.org/agi-curriculum/https://books.google.com/books?uid=111815788291054011027&as_coll=1012&source=gbs_lp_bookshelf_listhttps://books.google.com/books?uid=111815788291054011027&as_coll=1012&source=gbs_lp_bookshelf_list