In this post we describe the Nyström method for finding the eigenvalues and eigenfunctions of a kernel function. This has ...

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In this post we describe the use of momentum to speed up gradient descent. We first describe the intuition for ...

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Popular papers often have code on Github, but the authors are super busy writing new papers, so you may notice ...

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In this post we describe how to do gradient descent with constraints. We first describe the problem, including why we ...

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In this post we describe the high-level idea behind gradient descent for convex optimization. Much of the intuition comes from ...

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An important class of machine learning models is decision trees: you can use them for both classification and regression. In ...

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In this post we describe several methods for visualizing time series data. Time series visualization has several uses. First, it ...

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In this post we describe basic visualization of missing data patterns in R with VIM. We describe how to see ...

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In this post we describe stationary and non-stationary time series. We first ask why we want stationarity, then describe stationarity ...

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In this post we describe the basics of time series smoothing in R. We first describe why to do smoothing, ...

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In this post we describe how to solve the full rank least squares problem without inverting a matrix, as inverting ...

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In this post we describe the basics of 1-d convolutional neural networks, which can be used in time series forecasting ...

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In this post we describe multilayer perceptrons. We first describe why we want to use neural networks, and what feedforward ...

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In this post we describe the basics of missing data. We first ask whether we should consider the data to ...

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In this post we describe the autoregressive (AR) time series model. We define it, describe steps to take before fitting ...

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In this post, we describe Granger causality, which helps us answer the question of whether one time series is useful ...

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In this post we describe the basics of long-short term memory (LSTM). We first describe some alternative classical approaches and ...

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In this post, we describe how to compare linear regression models between two groups. Without Regression: Testing Marginal Means Between ...

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In this post we show how to predict future measurement values in a longitudinal setting using linear mixed models (LMMs) ...

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In this post we describe centering features in linear regression: you should do it because it changes the interpretation of ...

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In this post we briefly describe some interesting looking companies doing machine learning in the healthcare space. Flatiron Health Flatiron ...

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In this post we describe the problem of class imbalance in classification datasets, how it affects classifier learning as well ...

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In this post we describe how to do binary classification in R, with a focus on logistic regression. Some of ...

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Introduction to Linear Regression Summary Printouts In this post we describe how to interpret the summary of a linear regression ...

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In this article we discuss how to evaluate classification accuracy, with a focus on binary classification and using software from ...

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In this post we describe how to do regression with count data using R. In many applications we want to ...

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In this post we describe the Kaplan Meier non-parametric estimator of the survival function. We first describe what problem it ...

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In this article we describe how to perform linear regression. We go over some linear regression basics and answer the ...

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There are several reasons to log transform the response. The obvious one is to fix linearity violations, but in many ...

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In linear regression, you fit the model \begin{align}y=X\beta+\epsilon\end{align} However, often the relationship between your $x$ and $y$ variables is not ...

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In this post we analyze the residuals vs leverage plot. This can help detect outliers in a linear regression model ...

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In this post we describe how to analyze a scale location plot. You may also be interested in the fitted ...

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In this post we describe how to interpret a QQ plot, including how the comparison between empirical and theoretical quantiles ...

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In this post we describe the fitted vs residuals plot, which allows us to detect several types of violations in ...

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Note: this is loosely based on Coursera's A Crash Course on Causality: Inferring Causal Effects from Observational Data Introduction In ...

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Background Classical statistics was developed to study how to collect and analyze data in the setting of controlled studies. However, ...

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You should use non-parametric tests when the most naive distributional assumptions of a parametric test fail and you can’t invoke ...

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We want to use wearable technology in healthcare to drive interventions. For example, an app might send a notification encouraging ...

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Machine learning and statistics in healthcare have potentially game changing applications, but also pose new challenges for modeling and analysis ...

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These are solutions to the intuition questions from Stanford's Convolutional Networks for Visual Recognition (Stanford CS 231n) assignment 1 inline ...

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This article is in part based on http://www2.stat.duke.edu/~sayan/Sta613/2017/lec/LMM.pdf. In this post we describe how linear mixed models can be used ...

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When you start you should learn a few basic algorithms and understand them well. Here are five good ones. Linear ...

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Machine learning and statistics use very similar tools: probability distributions, representations of conditional probability, maximum likelihood estimation, Bayesian inference, etc. ...

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In Survival Analysis, you have three options for modeling the survival function: non-parametric (such as Kaplan-Meier), semi-parametric (Cox regression), and ...

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One way to learn anything quickly is to constantly apprentice yourself to people better than you at what you're trying ...

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Math is difficult, but is extremely important for statistics and machine learning. Sometimes when you work with great researchers, they ...

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Logit and logistic regression are the same thing. However, they actually relate to generalized linear models. In a generalized linear ...

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You are testing a new drug treatment for HIV, and your new drug costs 10x the old one. You run ...

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In survival analysis, we want to model the time to a first event, often death. One way to model the ...

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Here are some books and courses for survival analysis that are useful for learning it. People of various skill levels ...

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Say you want to model the evolution over time of a disease like HIV in an individual, or the evolution ...

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I'd often seen two different versions of Cauchy Schwartz (CS). In analysis and linear algebra I'd learned that if $x,y$ ...

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I recently read the paper "SARA: A Mobile App to Engage Users in Health Data Collection." [1] The problem they ...

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An important area in applied and methodological statistics as well as machine learning is disease progression modeling. There are arguably ...

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Python is the language of choice for many data scientists and researchers who analyze data, and it has far superior ...

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Say you have an experiment and you want to test whether there is a difference in the treatment response between ...

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In this post we check the assumptions of linear regression using Python. Linear regression models the relationship between a design ...

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In a previous post, we introduced the basic terminology of hypothesis testing. We also wanted to test the null hypothesis ...

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Here we'll go over the fundamental concepts of hypothesis testing. Generally we want to test two hypotheses. Let's say we ...

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In a previous post, I mentioned some books that are useful if you want to eventually be able to read ...

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Here we'll talk about multicollinearity in linear regression. This occurs when there is correlation among features, and causes the learned ...

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I saw a question like this Quora, and have been meaning to start a blog so decided to answer it ...

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