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DSPA Chapter 20 Prediction and Internal Statistical Cross-validation

Chapter 20 uses Google Flu Trends, Autism, and Parkinson’s disease case-studies to illustrate (1) alternative forecasting types using linear and non-linear predictions, (2) exhaustive and…

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DSPA Chapter 18 Big Longitudinal Data Analysis (Timeseries GEE GLMM SEM)

DSPA Chapter 18 Big Longitudinal Data Analysis (Timeseries GEE GLMM SEM)The time-varying (longitudinal) characteristics of large information flows represent a special case of the complexity,…

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DSPA Chapter 17 (Regularized Linear Modeling and Controlled Variable Selection)

DSPA Chapter 17 (Regularized Linear Modeling and Controlled Variable Selection) Classical techniques for choosing important covariates to include in a model of complex multivariate data relied on…

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DSPA Chapter 12: k-Means Clustering

DSPA Chapter 12: k-Means Clustering In this chapter, we will present (1) clustering as a machine learning task, (2) the silhouette plots for classification evaluation, (3) the k-Means clustering…

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DSPA Chapter 5 Dimensionality Reduction

DSPA Chapter 5 Dimensionality Reduction Dimensionality reduction techniques enable exploratory data analyses by reducing the complexity of the dataset, still approximately preserving important…

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DSPA Chapter 7 Naive Bayes Classification

DSPA Chapter 7 Naive Bayes Classification Please review the introduction to Chapter 6, where we described the types of machine learning methods and presented lazy classification for numerical…

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DSPA Overview

DSPA Overview This Data Science and Predictive Analytics (DSPA) course is offered by Prof. Ivo Dinov at the University of Michigan. The package of materials collectively aim to provide learners…

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DSPA Introduction

Data Science and Predictive Analytics (DSPA) course This open graduate course will provide a general overview of the principles, concepts, techniques, tools and services for managing,…

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