
From Ivaylo Dinov Aug 30, 2017
Chapter 22 demonstrates the R deep learning package MXNetR and demonstrate stateoftheart deep learning models utilizing CPU and GPU for fast training (learning) and… 
From Ivaylo Dinov Aug 30, 2017
Chapter 21 covers (1) constrained and unconstrained optimization, (2) Lagrange multipliers, (3) linear, quadratic and (general) nonlinear programming, and (4) data… 
From Ivaylo Dinov Aug 30, 2017
Chapter 20 uses Google Flu Trends, Autism, and Parkinson’s disease casestudies to illustrate (1) alternative forecasting types using linear and nonlinear… 
From Ivaylo Dinov Jul 17, 2017
DSPA Chapter 19 Text Mining (TM) and Natural Language Processing (NLP)Natural Language Processing (NLP) and Text Mining (TM) refer to automated machinedriven… 
From Ivaylo Dinov Jul 17, 2017
DSPA Chapter 18 Big Longitudinal Data Analysis (Timeseries GEE GLMM SEM)The timevarying (longitudinal) characteristics of large information flows represent a special… 
From Ivaylo Dinov Jul 17, 2017
DSPA Chapter 16 (Variable Selection) As we mentioned in Chapter 15, variable selection is very important when dealing with bioinformatics, healthcare, and biomedical… 
From Ivaylo Dinov Jul 17, 2017
DSPA Chapter 17 (Regularized Linear Modeling and Controlled Variable Selection) Classical techniques for choosing important covariates to include in a model of complex… 
From Ivaylo Dinov Jul 17, 2017
DSPA Chapter 4 Linear Algebra and Matrix Computing Linear algebra is a branch of mathematics that studies linear associations using vectors, vectorspaces, linear… 
From Ivaylo Dinov Jul 17, 2017
DSPA Chapter 15 Specialized ML Techniques In this chapter, we will discuss some technical details about data formats, streaming, optimization of computation, and… 
From Ivaylo Dinov Jul 17, 2017
DSPA Chapter 12: kMeans Clustering In this chapter, we will present (1) clustering as a machine learning task, (2) the silhouette plots for classification evaluation,… 
From Ivaylo Dinov Jul 17, 2017
DSPA Chapter 13 Model Evaluation In this chapter, we will discuss (1) various evaluation strategies for prediction, clustering, classification, regression, and… 
From Ivaylo Dinov Jul 17, 2017
DSPA Chapter 14 Improvement of Model Performance We already explored several alternative machine learning (ML) methods for prediction, classification, clustering and… 
From Ivaylo Dinov Jul 17, 2017
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 Ivaylo Dinov Jul 17, 2017
DSPA Chapter 5 Dimensionality Reduction Dimensionality reduction techniques enable exploratory data analyses by reducing the complexity of the dataset, still… 
From Ivaylo Dinov Jul 17, 2017
DSPA Chapter 10 SVM Classification In this chapter, we are going to cover two very powerful machinelearning algorithms. These techniques have complex mathematical…