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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)…
DSPA Chapter 22 Deep Learning
From Tina Chang
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Chapter 21 covers (1) constrained and unconstrained optimization, (2) Lagrange multipliers, (3) linear, quadratic and (general) non-linear programming, and (4) data…
DSPA Chapter 21 Function Optimization
From Tina Chang
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Chapter 20 uses Google Flu Trends, Autism, and Parkinson’s disease case-studies to illustrate (1) alternative forecasting types using linear and non-linear…
DSPA Chapter 20 Prediction and Internal…
From Tina Chang
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DSPA Chapter 19 Text Mining (TM) and Natural Language Processing (NLP)Natural Language Processing (NLP) and Text Mining (TM) refer to automated machine-driven…
DSPA Chapter 19 Text Mining (TM) and Natural…
From Tina Chang
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DSPA Chapter 18 Big Longitudinal Data Analysis (Timeseries GEE GLMM SEM)The time-varying (longitudinal) characteristics of large information flows represent a special…
DSPA Chapter 18 Big Longitudinal Data Analysis…
From Tina Chang
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DSPA Chapter 16 (Variable Selection) As we mentioned in Chapter 15, variable selection is very important when dealing with bioinformatics, healthcare, and…
DSPA Chapter 16 (Variable Selection)
From Tina Chang
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DSPA Chapter 17 (Regularized Linear Modeling and Controlled Variable Selection) Classical techniques for choosing important covariates to include in a model of complex…
DSPA Chapter 17 (Regularized Linear Modeling and…
From Tina Chang
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DSPA Chapter 4 Linear Algebra and Matrix Computing Linear algebra is a branch of mathematics that studies linear associations using vectors, vector-spaces, linear…
DSPA Chapter 4 Linear Algebra and Matrix Computing
From Tina Chang
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DSPA Chapter 15 Specialized ML Techniques In this chapter, we will discuss some technical details about data formats, streaming, optimization of computation, and…
DSPA Chapter 15 Specialized ML Techniques
From Tina Chang
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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,…
DSPA Chapter 12: k-Means Clustering
From Tina Chang
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DSPA Chapter 13 Model Evaluation In this chapter, we will discuss (1) various evaluation strategies for prediction, clustering, classification, regression, and…
DSPA Chapter 13 Model Evaluation
From Tina Chang
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DSPA Chapter 14 Improvement of Model Performance We already explored several alternative machine learning (ML) methods for prediction, classification, clustering and…
DSPA Chapter 14 Improvement of Model Performance
From Tina Chang
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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…
DSPA Chapter 8 Decision Tree Classification
From Tina Chang
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DSPA Chapter 5 Dimensionality Reduction Dimensionality reduction techniques enable exploratory data analyses by reducing the complexity of the dataset, still…
DSPA Chapter 5 Dimensionality Reduction
From Tina Chang
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DSPA Chapter 10 SVM Classification In this chapter, we are going to cover two very powerful machine-learning algorithms. These techniques have complex mathematical…
DSPA Chapter 10: SVM Classification
From Tina Chang
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