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Rohan Kotwani
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  • New Orleans, Louisiana
  • United States
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Rohan Kotwani commented on Paolo Lulli's blog post Difference between Gradient Descent and Newton-Raphson
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Aug 4
Rohan Kotwani posted a blog post

Create Transformed, N-Dimensional Polygons with Covariance Matrix

The covariance matrix has many interesting properties, and it can be found in mixture models, component analysis, Kalman filters, and more. Developing an intuition for how the covariance matrix operates is useful in understanding its practical implications. This article will focus on a few important properties, associated proofs, and then some interesting practical applications, i.e., extracting transformed polygons from a Gaussian mixture's covariance matrix.I have often found that research…See More
May 30
Rohan Kotwani posted blog posts
May 26
Rohan Kotwani's blog post was featured

Create Transformed, N-Dimensional Polygons with Covariance Matrix

The covariance matrix has many interesting properties, and it can be found in mixture models, component analysis, Kalman filters, and more. Developing an intuition for how the covariance matrix operates is useful in understanding its practical implications. This article will focus on a few important properties, associated proofs, and then some interesting practical applications, i.e., extracting transformed polygons from a Gaussian mixture's covariance matrix.I have often found that research…See More
May 26

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Short Bio
Interested in camera automation, time series forecasting, kernel methods, and sequential models.
My Web Site Or LinkedIn Profile
http://www.linkedin.com/in/rkotwani
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Data Scentist
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Rohan Kotwani's Blog

Create Transformed, N-Dimensional Polygons with Covariance Matrix

Posted on May 26, 2019 at 7:30am 0 Comments

The covariance matrix has many interesting properties, and it can be found in mixture models, component analysis, Kalman filters, and more. Developing an intuition for how the covariance matrix operates is useful in understanding its practical implications. This article will focus on a few important properties, associated proofs, and then some interesting practical applications, i.e., extracting transformed polygons from a Gaussian mixture's covariance matrix.

I have often found that…

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High Density Region Estimation with KernelML

Posted on January 3, 2019 at 4:00pm 0 Comments

Data scientists and predictive modelers often use 1-D and 2-D aggregate statistics for exploratory analysis, data cleaning, and feature creation. Higher dimensional aggregations, i.e., 3 dimensional and above, are more difficult to visualize and understand. High density regions are one example of these N-dimensional statistics. High density regions can be useful for summarizing common characteristics across multiple variables. Another use case is to validate a forecast prediction’s…

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Generalized Machine Learning - Kerneml - Simple ML to train Complex ML

Posted on May 4, 2018 at 10:00am 2 Comments

I recently created a ‘particle optimizer’ and published a pip python package called kernelml. The motivation for making this algorithm was to give analysts and data scientists a generalized machine learning algorithm for complex loss functions and non-linear coefficients. The optimizer uses a combination of simple machine learning and probabilistic simulations to search for optimal parameters using a loss function, input and output matrices, and (optionally) a random…

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How signal processing can be used to identify patterns in complex time series

Posted on January 18, 2017 at 6:30pm 3 Comments

The trend and seasonality can be accounted for in a linear model by including sinusoidal components with a given frequency. However, finding the appropriate frequency…

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