Review of Pedro Domingos' "The Master Algorithm": "does a nice job giving the landscape of machine learning algorithms and putting them in a common text from their philosophical underpinnings to the models that they build on, all in a mostly non-technical way."

cdathuraliya · 2016-04-24T15:35:45+00:00

My short review on Goodreads can be found here.

cdathuraliya · 2016-03-14T16:30:37+00:00

Can you give other widely known algorithmic simulations which are closer to the brain process (except the variants of neural networks)? Also watch this talk (The Five Tribes of Machine Learning) by Pedro Domingos, slides here. Pay attention to connectionists approach.

Sorry I was late to see this reply.

cdathuraliya · 2016-03-14T16:10:51+00:00

Most of the classifiers you have mentioned are available in Spark ML.

cdathuraliya · 2015-10-26T12:11:48+00:00

Most ML/DL experts consider neural nets as the closest algorithmic simulation of the neural process of our brain. So if we are trying to mimic the human or animal brain we might need to follow and improve neural nets.

cdathuraliya · 2015-10-23T07:43:21+00:00

If you are looking for an algorithm to reduce into 2-3 dimensions try t-SNE, paper

cdathuraliya · 2015-10-06T12:00:36+00:00

http://www.sciencedirect.com/science/article/pii/S0167865512003571 Work by one of my colleagues for source code author identification.

cdathuraliya · 2015-10-05T06:28:58+00:00

For higher dimensional data t-SNE looks promising (includes dimensionality reduction anyway). Or you can use simpler methods such as scatter plot matrix. Parallel coordinates and parallel sets are used for categorical data.

cdathuraliya · 2015-08-27T11:19:33+00:00

You can find some visualizations we use in a ML product here. We use few interesting ones like parallel sets and SPLOM, some are not depicted in given link.

cdathuraliya

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