Blohm Lab Wiki - User contributions [en-ca]

File:Repeated ANOVA MATLAB v2.pdf

2018-10-18T17:09:27Z

Parisa: Parisa uploaded a new version of File:Repeated ANOVA MATLAB v2.pdf

Learning Resources

2018-10-18T17:08:22Z

Parisa:

== General Computational Neuroscience ==
* [https://www.youtube.com/user/elscvideo/videos ELSC Youtube Channel] Contains archived computational neuroscience seminars and lectures, including Dayan, Abbot, Pouget..., as well as physiology resources.
* [https://www.coursera.org/learn/computational-neuroscience Coursera Computational Neuroscience] With instructors Rajesh Rao and Adrienne Fairhall.
* [http://www.shadmehrlab.org/lectures.html Reza Shadmehr lectures on motor control & learning]

== Machine Learning ==
==== Books ====
* [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]
* [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book]
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
* [https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf Murphy's book]
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]
* [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]; also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book], which has less focus on mathematical foundations and more on applications (in R).
* [http://jim-stone.staff.shef.ac.uk/BookBayes2012/books_by_jv_stone/index.html Books] by J V Stone.

==== Online Resources ====
* [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --> free enrollment)]
* [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]
* [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]
* [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop
* [http://www.arxiv-sanity.com arXiv Sanity Preserver], an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.
* Microsoft [https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/ Professional Program] for Artificial Intelligence (free to audit)

==== Journal Club Tutorials ====
See [[Journal Club#Tutorials]].

== General Math ==
* [https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf Matrix cookbook]
* [http://www.cs.toronto.edu/~urtasun/courses/CV/lecture02.pdf Image filtering, edge detection, etc. (Computer vision)]
* [http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm Hough transform tutorial (Computer vision)]
* [http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf Introductory tutorial on Wavelet transforms]
*[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]
* [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]

== MATLAB ==
* [http://www.mathworks.com/moler/exm/chapters.html?refresh=true Moler's tutorials]
* [[Media:Matlab_intro.pdf | Scott Murdison's compilation]]
* [[Media:Curve_Fitting.pdf | Jerry Jeyachandra's Curve Fitting Tutorial]]
**[[Media:CurveFit_Tutorial.zip | Curve Fitting Scripts]]

== Statistics ==
* [http://onlinestatbook.com/2/index.html Rice University online Stats book]
* [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book
* [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods & significance estimation]
* how to do [[Media:Repeated_ANOVA_MATLAB_v2.pdf | repeated measures ANOVA]] in Matlab (by Parisa)
* [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice]
* [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions]
* [[Media:Latin_square_Method.pdf | Latin square method for experimental design]]

File:Repeated ANOVA MATLAB v2.pdf

2018-10-18T17:05:14Z

Parisa:

Useful stuff

2016-12-12T16:02:25Z

Parisa: /* Statistics */

A list of useful resources on the web.

== General Math ==

* [https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf Matrix cookbook]
* [http://www.cs.toronto.edu/~urtasun/courses/CV/lecture02.pdf Image filtering, edge detection, etc. (Computer vision)]
* [http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm Hough transform tutorial (Computer vision)]
* [http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf Introductory tutorial on Wavelet transforms]
*[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]
* [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]
----

== Statistics ==
* [http://onlinestatbook.com/2/index.html Rice University online Stats book]
* [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book
* [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods & significance estimation]
* how to do [[Media:Repeated_ANOVA_MATLAB.pdf | repeated measures ANOVA]] in Matlab (by Parisa)
* [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice]
* [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions]
* [[Media:Latin_square_Method.pdf | Latin square method for experimental design]]
----

== How to do science ==
* [http://klab.smpp.northwestern.edu/wiki/index.php5/Paper_Writing_101 Konrad's paper writing 101]
* [http://collections.plos.org/ten-simple-rules PLoS CB - 10 simple rules collection]: a must read for everyone!
* [http://colorbrewer2.org Colorbrewer]: great tool for selecting colour schemes on publications
----

== How to be successful ==
* [https://www.technologyreview.com/s/409043/how-to-think/?utm_content=buffer9ea8e&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer Ed Boyden's advice on how to think]
* [http://www.wikihow.com/Think Developing better thought processes]
* [http://faculty.georgetown.edu/kingch/How_to_Think.htm How to argue well]
* [http://www.lifehack.org/articles/productivity/12-weekend-habits-highly-successful-people.html Habits for success]

Useful stuff

2016-12-12T16:00:09Z

Parisa: /* Statistics */

A list of useful resources on the web.

== General Math ==

* [https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf Matrix cookbook]
* [http://www.cs.toronto.edu/~urtasun/courses/CV/lecture02.pdf Image filtering, edge detection, etc. (Computer vision)]
* [http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm Hough transform tutorial (Computer vision)]
* [http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf Introductory tutorial on Wavelet transforms]
*[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]
* [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]
----

== Statistics ==
* [http://onlinestatbook.com/2/index.html Rice University online Stats book]
* [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book
* [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods & significance estimation]
* how to do [[Media:Repeated_ANOVA_MATLAB.pdf | repeated measures ANOVA]] in Matlab (by Parisa)
* [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice]
* [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions]
* [[Media:Latin_Square_Method.pdf Latin square method for experimental design]]
----

== How to do science ==
* [http://klab.smpp.northwestern.edu/wiki/index.php5/Paper_Writing_101 Konrad's paper writing 101]
* [http://collections.plos.org/ten-simple-rules PLoS CB - 10 simple rules collection]: a must read for everyone!
* [http://colorbrewer2.org Colorbrewer]: great tool for selecting colour schemes on publications
----

== How to be successful ==
* [https://www.technologyreview.com/s/409043/how-to-think/?utm_content=buffer9ea8e&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer Ed Boyden's advice on how to think]
* [http://www.wikihow.com/Think Developing better thought processes]
* [http://faculty.georgetown.edu/kingch/How_to_Think.htm How to argue well]
* [http://www.lifehack.org/articles/productivity/12-weekend-habits-highly-successful-people.html Habits for success]

Useful stuff

2016-12-12T15:57:34Z

Parisa: /* Statistics */

A list of useful resources on the web.

== General Math ==

* [https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf Matrix cookbook]
* [http://www.cs.toronto.edu/~urtasun/courses/CV/lecture02.pdf Image filtering, edge detection, etc. (Computer vision)]
* [http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm Hough transform tutorial (Computer vision)]
* [http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf Introductory tutorial on Wavelet transforms]
*[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]
* [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]
----

== Statistics ==
* [http://onlinestatbook.com/2/index.html Rice University online Stats book]
* [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book
* [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods & significance estimation]
* how to do [[Media:Repeated_ANOVA_MATLAB.pdf | repeated measures ANOVA]] in Matlab (by Parisa)
* [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice]
* [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions]
* [[Media:Latin_Square_Method.pdf | Latin square method for experimental design]]
----

== How to do science ==
* [http://klab.smpp.northwestern.edu/wiki/index.php5/Paper_Writing_101 Konrad's paper writing 101]
* [http://collections.plos.org/ten-simple-rules PLoS CB - 10 simple rules collection]: a must read for everyone!
* [http://colorbrewer2.org Colorbrewer]: great tool for selecting colour schemes on publications
----

== How to be successful ==
* [https://www.technologyreview.com/s/409043/how-to-think/?utm_content=buffer9ea8e&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer Ed Boyden's advice on how to think]
* [http://www.wikihow.com/Think Developing better thought processes]
* [http://faculty.georgetown.edu/kingch/How_to_Think.htm How to argue well]
* [http://www.lifehack.org/articles/productivity/12-weekend-habits-highly-successful-people.html Habits for success]

File:Latin square Method.pdf

2016-12-12T15:55:13Z

Parisa:

Machine learning

2016-06-27T19:14:03Z

Parisa:

Our Journal Club slides about ML will be posted here.

* [[Media:MLintro.pdf | Machine learning introduction]] - Gunnar
* [[Media:LinearRegression.pdf | Linear Regression]] - Sisi
* [[Media:Linear_Classification.pdf | Linear Classification]] - Parisa
* [[Media:MCMC.pdf | Markov Chain Montre Carlo]] - Gunnar
* [[Media:Intro to PCA and ICA.pdf | Introduction to PCA and ICA]] - Cindy
* [[Media:EM_algorithm.pdf | Expectation Maximization]] - Brandon and Jonathan
* [[Media:HMM-parisa.pdf | Hidden Markov Models]] - Parisa
* [[Media:KalmanFilter.pdf | Kalman filter]] - Josh
* [[Media:ML-ANNs.pdf | Rate-based networks & error back-propagation learning]] - Scott
* [[Media:Support Vector Machines (SVM).pdf | Support Vector Machines]] - Jerry
* [[Media:Deep_Belief_Network_Home_(2).pdf | Deep belief networks ]] - Tiger

----

== Machine learning books ==

* [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]
* [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book]
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
* [https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf Murphy's book]
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]

----

File:Deep Belief Network Home (2).pdf

2016-06-27T19:13:02Z

Parisa:

Machine learning

2016-06-21T19:33:56Z

Parisa:

Our Journal Club slides about ML will be posted here.

* [[Media:MLintro.pdf | Machine learning introduction]] - Gunnar
* [[Media:LinearRegression.pdf | Linear Regression]] - Sisi
* [[Media:Linear_Classification.pdf | Linear Classification]] - Parisa
* [[Media:MCMC.pdf | Markov Chain Montre Carlo]] - Gunnar
* [[Media:Intro to PCA and ICA.pdf | Introduction to PCA and ICA]] - Cindy
* [[Media:EM_algorithm.pdf | Expectation Maximization]] - Brandon and Jonathan
* [[Media:HMM-parisa.pdf | Hidden Markov Models]] - Parisa
* [[Media:KalmanFilter.pdf | Kalman filter]] - Josh
* [[Media:ML-ANNs.pdf | Rate-based networks & error back-propagation learning]] - Scott
* [[Media:Support Vector Machines (SVM).pdf | Support Vector Machines]] - Jerry
* Deep belief networks

----

== Machine learning books ==

* [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]
* [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book]
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
* [https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf Murphy's book]
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]

----

Machine learning

2016-06-21T19:33:38Z

Parisa:

Our Journal Club slides about ML will be posted here.

* [[Media:MLintro.pdf | Machine learning introduction]] - Gunnar
* [[Media:LinearRegression.pdf | Linear Regression]] - Sisi
* [[Media:Linear_Classification.pdf | Linear Classification]] - Parisa
* [[Media:MCMC.pdf | Markov Chain Montre Carlo]] - Gunnar
* [[Media:Intro to PCA and ICA.pdf | Introduction to PCA and ICA]] - Cindy
* [[Media:EM_algorithm.pdf | Expectation Maximization]] - Brandon and Jonathan
* [[Media:HMM-parisa.pdf | Hidden Markov Models]] - Parisa
* [[Media:KalmanFilter.pdf | Kalman filter]] - Josh
* [[Media:ML-ANNs.pdf | Rate-based networks & error back-propagation learning]] - Scott
* [[Media:Support Vector Machines (SVM).pdf | Support Vector Machines]] - Jerry
* Deep belief networks
* Supervised vs. unsupervised learning

----

== Machine learning books ==

* [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]
* [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book]
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
* [https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf Murphy's book]
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]

----

File:EM algorithm.pdf

2016-06-21T19:31:23Z

Parisa:

File:HMM-parisa.pdf

2016-05-24T17:36:05Z

Parisa: Hidden Markov Models

Hidden Markov Models

Machine learning

2016-05-24T17:35:15Z

Parisa:

Our Journal Club slides about ML will be posted here.

* [[Media:MLintro.pdf | Machine learning introduction]] - Gunnar
* [[Media:LinearRegression.pdf | Linear Regression]] - Sisi
* [[Media:Linear_Classification.pdf | Linear Classification]] - Parisa
* [[Media:MCMC.pdf | Markov Chain Montre Carlo]] - Gunnar
* [[Media:Intro to PCA and ICA.pdf | Introduction to PCA and ICA]] - Cindy
* Expectation Maximization - Brandon and Jonathan
* [[Media:HMM-parisa.pdf | Hidden Markov Models]] - Parisa
* [[Media:ML-ANNs.pdf | Rate-based networks & error back-propagation learning]] - Scott
* Support Vector Machines - Jerry
* Deep belief networks
* Supervised vs. unsupervised learning

----

== Machine learning books ==

* [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]
* [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book]
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
* [https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf Murphy's book]
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]

----

Machine learning

2016-04-20T19:35:34Z

Parisa:

Our Journal Club slides about ML will be posted here.

* [[Media:MLintro.pdf | Machine learning introduction]] - Gunnar
* [[Media:LinearRegression.pdf | Linear Regression]] - Sisi
* [[Media:Linear_Classification.pdf | Linear Classification]] - Parisa
* [[Media:MCMC.pdf | Markov Chain Montre Carlo]] - Gunnar
* [[Media:Intro to PCA and ICA.pdf | Introduction to PCA and ICA]] - Cindy
* Expectation Maximization - Brandon and Jonathan
* Hidden Markov models
* Rate-based networks & error back-propagation learning - Scott
* Support Vector Machines - Jerry
* Deep belief networks
* Supervised vs. unsupervised learning

----

== Machine learning books ==

* [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]
* [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book]
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
* [https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf Murphy's book]
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]

----

Machine learning

2016-04-20T18:50:49Z

Parisa:

Our Journal Club slides about ML will be posted here.

* [[Media:MLintro.pdf | Machine learning introduction]] - Gunnar
* [[Media:LinearRegression.pdf | Linear Regression]] - Sisi
* [[Media:Linear_Classification.pdf | Linear Classification]] - Parisa
* [[Media:MCMC.pdf | Markov Chain Montre Carlo]] - Gunnar
* [[Media:Intro to PCA and ICA.pdf | Introduction to PCA and ICA]] - Cindy

----

== Machine learning books ==

* [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]
* [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book]
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
* [https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf Murphy's book]
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]

----

File:LinearRegression.pdf

2016-04-20T18:49:23Z

Parisa:

Machine learning

2016-04-11T20:54:29Z

Parisa:

Our Journal Club slides about ML will be posted here.

* [[Media:MLintro.pdf | Machine learning introduction]] - Gunnar
* Linear Regression - Sisi
* [[Media:Linear_Classification.pdf | Linear Classification]] - Parisa
* [[Media:MCMC.pdf | Markov Chain Montre Carlo]] - Gunnar
* [[Media:Intro to PCA and ICA.pdf | Introduction to PCA and ICA]] - Cindy

----

== Machine learning books ==

* [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]
* [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book]
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
* [https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf Murphy's book]
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]

----

File:Intro to PCA and ICA.pdf

2016-04-11T20:54:02Z

Parisa:

Machine learning

2016-04-11T20:51:22Z

Parisa:

Our Journal Club slides about ML will be posted here.

* [[Media:MLintro.pdf | Machine learning introduction]] - Gunnar
* Linear Regression - Sisi
* [[Media:Linear_Classification.pdf | Linear Classification]] - Parisa
* [[Media:MCMC.pdf | Markov Chain Montre Carlo]] - Gunnar
* [[Media:Intro to PCA and ICA.pptx | Introduction to PCA and ICA]] - Cindy

----

== Machine learning books ==

* [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]
* [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book]
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
* [https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf Murphy's book]
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]

----

File:Intro to PCA and ICA.pptx

2016-04-11T20:49:28Z

Parisa:

Machine learning

2016-04-06T23:31:11Z

Parisa:

File:Linear Classification.pdf

2016-04-06T23:29:14Z

Parisa: