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DSPA Chapter 22 Deep Learning

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) and testing (validation).

From  Tina Chang on August 30th, 2017 0 likes 78 plays 0  

DSPA Chapter 21 Function Optimization

Chapter 21 covers (1) constrained and unconstrained optimization, (2) Lagrange multipliers, (3) linear, quadratic and (general) non-linear programming, and (4) data denoising.

From  Tina Chang on August 30th, 2017 0 likes 20 plays 0  

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…

From  Tina Chang on August 30th, 2017 0 likes 31 plays 0  

DSPA Chapter 19 Text Mining (TM) and Natural Language Processing (NLP)

DSPA Chapter 19 Text Mining (TM) and Natural Language Processing (NLP)Natural Language Processing (NLP) and Text Mining (TM) refer to automated machine-driven algorithms for semantically mapping,…

From  Tina Chang on July 17th, 2017 0 likes 52 plays 0  

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, dynamic…

From  Tina Chang on July 17th, 2017 0 likes 30 plays 0  

DSPA Chapter 16 (Variable Selection)

DSPA Chapter 16 (Variable Selection) As we mentioned in Chapter 15, variable selection is very important when dealing with bioinformatics, healthcare, and biomedical data where we may have more…

From  Tina Chang on July 17th, 2017 0 likes 0 plays 0  

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…

From  Tina Chang on July 17th, 2017 0 likes 0 plays 0  

DSPA Chapter 4 Linear Algebra and Matrix Computing

DSPA Chapter 4 Linear Algebra and Matrix Computing Linear algebra is a branch of mathematics that studies linear associations using vectors, vector-spaces, linear equations, linear transformations…

From  Tina Chang on July 17th, 2017 0 likes 58 plays 0  

DSPA Chapter 15 Specialized ML Techniques

DSPA Chapter 15 Specialized ML Techniques In this chapter, we will discuss some technical details about data formats, streaming, optimization of computation, and distributed deployment of…

From  Tina Chang on July 17th, 2017 1 likes 13 plays 0  

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…

From  Tina Chang on July 17th, 2017 0 likes 0 plays 0  

DSPA Chapter 13 Model Evaluation

DSPA Chapter 13 Model Evaluation In this chapter, we will discuss (1) various evaluation strategies for prediction, clustering, classification, regression, and decision trees, (2) visualization of…

From  Tina Chang on July 17th, 2017 0 likes 13 plays 0  

DSPA Chapter 14 Improvement of Model Performance

DSPA Chapter 14 Improvement of Model Performance We already explored several alternative machine learning (ML) methods for prediction, classification, clustering and outcome forecasting. In many…

From  Tina Chang on July 17th, 2017 0 likes 16 plays 0  

DSPA Chapter 8 Decision Tree Classification

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 decision-tree divide and conquer…

From  Tina Chang on July 17th, 2017 0 likes 40 plays 0  

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…

From  Tina Chang on July 17th, 2017 0 likes 64 plays 0  

DSPA Chapter 10: SVM Classification

DSPA Chapter 10 SVM Classification In this chapter, we are going to cover two very powerful machine-learning algorithms. These techniques have complex mathematical formulations, however, efficient…

From  Tina Chang on July 17th, 2017 0 likes 36 plays 0  

DSPA Chapter 11 Apriori Association Rule Learning

DSPA Chapter 11 Apriori Association Rule Learning HTTP cookies are used to track web-surfing the Internet traffic. We often notice that promotions (ads) on websites tend to match our needs, reveal…

From  Tina Chang on July 17th, 2017 0 likes 11 plays 0  

DSPA Chapter 3 Data Visualization

DSPA Chapter 3 Data Visualization In this chapter, we use a broad range of simulations and hands-on activities to highlight some of the basic data visualization techniques using R. A brief…

From  Tina Chang on July 17th, 2017 0 likes 107 plays 0  

DSPA Chapter 6: k-Nearest Neighbors Classification

DSPA Chapter 6: k-Nearest Neighbors Classification In the next several chapters we will concentrate of various progressively advanced machine learning, classificaiton and clustering techniques.…

From  Tina Chang on July 17th, 2017 0 likes 49 plays 0  

DSPA Chapter 9: Regression Classification

DSPA Chapter 9: Regression Classification In previous chapters (6, 7, and 8), we covered some classification methods that use mathematical formalism to address everyday life prediction problems.…

From  Tina Chang on July 17th, 2017 0 likes 44 plays 0  

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…

From  Tina Chang on July 17th, 2017 0 likes 36 plays 0