# Difference between revisions of "Journal Club"

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* [March 22, 2018] Dasgupta, Stevens, Navlakha, 2017. A neural algorithm for a fundamental computing problem. [http://science.sciencemag.org/content/358/6364/793] | * [March 22, 2018] Dasgupta, Stevens, Navlakha, 2017. A neural algorithm for a fundamental computing problem. [http://science.sciencemag.org/content/358/6364/793] | ||

− | == Tutorials == | + | == NEW: Modern ML tutorials == |

+ | see Markdown slides and code [https://github.com/BlohmLab/MLtutorials here] | ||

+ | |||

+ | == Older Tutorials == | ||

* [[Media:MLintro.pdf | Machine learning introduction]] - Gunnar | * [[Media:MLintro.pdf | Machine learning introduction]] - Gunnar | ||

* [[Media:LinearRegression.pdf | Linear Regression]] - Sisi | * [[Media:LinearRegression.pdf | Linear Regression]] - Sisi | ||

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* [[Media:Support Vector Machines (SVM).pdf | Support Vector Machines]] - Jerry | * [[Media:Support Vector Machines (SVM).pdf | Support Vector Machines]] - Jerry | ||

* [[Media:Deep_Belief_Network_Home_(2).pdf | Deep belief networks ]] - Tiger | * [[Media:Deep_Belief_Network_Home_(2).pdf | Deep belief networks ]] - Tiger | ||

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## Latest revision as of 19:36, 20 March 2020

## Articles

- [Aug 30, 2017] Shmuelof and Krakauer, 2011. Are we ready for a natural history of motor learning? Media:Journal_Club_Aug_30_2017.pdf Paper
- [Sept 11, 2017] Thura and Cisek, 2017. The Basal Ganglia Do Not Select Reach Targets but Control the Urgency of Commitment. File:JC Sept 11.pdf

- [Jan 11, 2018] Munafò et al., 2017. A manifesto for reproducible science. [1]
- [March 8, 2018] Castañón et al., 2018 (preprint). Human noise blindness drives suboptimal cognitive inference. [2]
- [March 15, 2018] Odegaard et al., 2017. Superior colliculus neuronal ensemble activity signals optimal rather than subjective confidence. [3]
- [March 22, 2018] Dasgupta, Stevens, Navlakha, 2017. A neural algorithm for a fundamental computing problem. [4]

## NEW: Modern ML tutorials

see Markdown slides and code here

## Older Tutorials

- Machine learning introduction - Gunnar
- Linear Regression - Sisi
- Linear Classification - Parisa
- Markov Chain Monte Carlo - Gunnar
- Introduction to PCA and ICA - Cindy
- Expectation Maximization - Brandon and Jonathan
- Hidden Markov Models - Parisa
- Kalman filter - Josh
- Rate-based networks & error back-propagation learning - Scott
- Support Vector Machines - Jerry
- Deep belief networks - Tiger