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From Tina Chang
Chapter 22 demonstrates the R deep learning package MXNetR and demonstrate state-of-the-art deep learning models utilizing CPU and GPU for fast training (learning)… -
From Tina Chang
Chapter 21 covers (1) constrained and unconstrained optimization, (2) Lagrange multipliers, (3) linear, quadratic and (general) non-linear programming, and (4) data… -
From Tina Chang
Chapter 20 uses Google Flu Trends, Autism, and Parkinson’s disease case-studies to illustrate (1) alternative forecasting types using linear and non-linear… -
From Tina Chang
DSPA Chapter 19 Text Mining (TM) and Natural Language Processing (NLP)Natural Language Processing (NLP) and Text Mining (TM) refer to automated machine-driven… -
From Tina Chang
DSPA Chapter 18 Big Longitudinal Data Analysis (Timeseries GEE GLMM SEM)The time-varying (longitudinal) characteristics of large information flows represent a special… -
From Tina Chang
DSPA Chapter 16 (Variable Selection) As we mentioned in Chapter 15, variable selection is very important when dealing with bioinformatics, healthcare, and… -
From Tina Chang
DSPA Chapter 17 (Regularized Linear Modeling and Controlled Variable Selection) Classical techniques for choosing important covariates to include in a model of complex… -
From Tina Chang
DSPA Chapter 4 Linear Algebra and Matrix Computing Linear algebra is a branch of mathematics that studies linear associations using vectors, vector-spaces, linear… -
From Tina Chang
DSPA Chapter 15 Specialized ML Techniques In this chapter, we will discuss some technical details about data formats, streaming, optimization of computation, and… -
From Tina Chang
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,… -
From Tina Chang
DSPA Chapter 13 Model Evaluation In this chapter, we will discuss (1) various evaluation strategies for prediction, clustering, classification, regression, and… -
From Tina Chang
DSPA Chapter 14 Improvement of Model Performance We already explored several alternative machine learning (ML) methods for prediction, classification, clustering and… -
From Tina Chang
DSPA Chapter 8 Decision Tree Classification In this chapter, we will (1) see a simple motivational example of decision trees based on the Iris data, (2) describe… -
From Tina Chang
DSPA Chapter 5 Dimensionality Reduction Dimensionality reduction techniques enable exploratory data analyses by reducing the complexity of the dataset, still… -
From Tina Chang
DSPA Chapter 10 SVM Classification In this chapter, we are going to cover two very powerful machine-learning algorithms. These techniques have complex mathematical…
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