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Statistical Software – Pros/Cons Comparison

Getting started

Install Basic Shell-based R

GUI based R Invocation (RStudio)

RStudio GUI Layout

Help

Simple Long-to-Wide Data format translation

Data generation

I/O

Slicing and extracting data

Variable conversion

Variable information

Data selection and manipulation

Math Functions

Matrix Operations

Advanced Data Processing

Strings

Plotting

QQ Normal Probability Plots

Low-level plotting commands

Graphics parameters

Optimization and model fitting

Statistics

Distributions

Programming

Data Simulation Primer

II. Managing data with R

Managing Data in R

Saving and Loading R Data Structures

Importing and Saving Data from CSV Files

Exploring the Structure of Data

Exploring Numeric Variables

Measuring the Central Tendency - mean and median

Measuring Spread - quartiles and the five-number summary

Visualizing Numeric Variables - boxplots

Visualizing Numeric Variables - histograms

Understanding Numeric Data - uniform and normal distributions

Measuring Spread - variance and standard deviation

Exploring Categorical Variables

Measuring the Central Tendency - the mode

Exploring Relationships Between Variables

Missing Data

Parsing webpages and visualizing tabular HTML data

Cohort-Rebalancing (for Imbalanced Groups)

III. Data Visualization

Classification of visualization methods

Composition

Histograms and density plots

Pie Chart

Heat map

Comparison

Paired ScatterPlots

Barplots

Trees and Graphs

Correlation Plots

Relationships

Line plots using ggplot

Density Plots

Distributions

2D Kernel Density and 3D Surface Plots

Jitter plot

Appendix

Hands-on Activity (Health Behavior Risks)

IV. Linear Algebra & Matrix Computing

Linear Algebra & Matrix Computing

Building Matrices

Create matrices

Adding columns and rows

Matrix subscripts

Matrix Operations

Addition

Subtraction

Multiplication

Elementwise multiplication

Matrix multiplication

Division

Transpose

Inverse

Matrix Operations

Matrix Algebra Notation

Matrix Notation

Solving Systems of Equations

The identity matrix

Vectors, Matrices, and Scalars

Sample Statistics

Mean

Variance

Applications of Matrix Algebra: Linear modeling

Finding function extrema (min/max) using calculus

Least Square Estimation

The R lm Function

Eigenvalues and Eigenvectors

Other important functions

Matrix notation

Linear regression

Sample covariance matrix

V. Dimensionality Reduction

Principal Component Analysis (PCA)

Independent Component Analysis (ICA)

Factor Analysis (FA)

Singular Value Decomposition (SVD)

VI. Lazy Learning – Classification Using Nearest Neighbors

Understanding classification using nearest neighbors

The kNN algorithm

Calculating distance

Choosing an appropriate k

Preparing data for use with kNN

Why is the kNN algorithm lazy?

Predictive Diagnostics

VII. Probabilistic Learning – Classification Using Naive Bayes

The Naive Bayes Algorithm

Assumptions

Bayes Formula

The Laplace Estimator

Case Study: Head and Neck Cancer Medication

VIII. Divide and Conquer – Classification Using Decision Trees

Understanding decision trees

Divide and conquer

The C5.0 decision tree algorithm

Choosing the best split

Pruning the decision tree

Boosting the accuracy of decision trees

Making some mistakes more costly than others

Understanding classification rules

Separate and conquer

The One Rule algorithm

The RIPPER algorithm

Rules from decision trees

IX. Forecasting Numeric Data – Regression Methods

Simple linear regression

Ordinary least squares estimation

Correlations

Multiple Linear Regression

Case Study 1: Baseball Players

Step 2 - exploring and preparing the data

Step 3 - training a model on the data

Step 4 - evaluating model performance

Step 5 - improving model performance

Regression trees and model trees

Heart Attack Data

X. Black Box Methods – Neural Networks and Support Vector Machines

Neural Networks

Network topology

Training neural networks with backpropagation

Case Study 1: Google Trends and the Stock Market

Support Vector Machines (SVM)

Case Study 2: Optical Character Recognition (OCR)

Case Study 3: Iris Flowers

XI. Apriori Algorithm for Association Rule Learning

Association Rules

Rule support and confidence

Case Study 1: Head and Neck Cancer Medications

Practice Problems: Groceries

XII. k-Means Clustering

Clustering as a machine learning task

The k-Means Clustering Algorithm

Case Study 1: Divorce and Consequences on Young Adults

Case study 2: Pediatric Trauma

Practice Problem: Youth Development

XIII. Evaluating Model Performance

Measuring performance for classification

Working with classification prediction data

Evaluation: Confusion matrices

Other performance measures

Visualizing performance tradeoffs

Estimating future performance (internal statistical validation)

The holdout method

XIV. Improving Model Performance

Tuning stock models for better performance

Using caret for automated parameter tuning

Creating a simple tuned model

Customizing the tuning process

Improving model performance with meta-learning

Understanding ensembles

Bagging

Boosting

Random forests

Training random forests

Evaluating random forest performance

XV. Specialized Machine Learning Topics: Data Formats and Optimization of Computation

Working with specialized data and databases

Querying data in SQL databases

Downloading the complete text of web pages

Web-page Data Scraping

Parsing JSON from web APIs

Reading and writing Microsoft Excel spreadsheets using XLSX

Visualizing network data

Optimization and improving the computational performance

Generalizing tabular data structures with dplyr

Parallel computing

GPU computing

XVI. Variable/Feature Selection

Variable selection methods

Case Study - ALS

Evaluating model performance

XVII. Regularized Linear Modeling and Knockoff Filtering

Regularized Linear Modeling

Ridge Regression

Least Absolute Shrinkage and Selection Operator (LASSO) Regression

Linear Regression

Assessing Prediction Accuracy

Estimating Prediction Error

Improving Prediction Accuracy

General Regularization Framework

Example: Neuroimaging-genetics study of Parkinson's Disease Dataset

Computational Complexity

n-Fold Cross Validation

Knock-off Filtering: Simulated Example

PD Neuroimaging-genetics Case-Study

Visualization

XVIII. Big Longitudinal Data Analysis

Time series analysis

Identifying the Diff, AR and MA parameters

Structural Equation Modeling (SEM)

Case study - Parkinson's Disease (PD)

Linear Mixed model

GLMM and GEE Longitudinal data analysis

XIX. Text Mining & Natural Language Processing

Term Frequency (TF), Inverse Document Frequency (IDF)

Document Term Matrix (DTM)

Case-Study: Job ranking

NLP

XX. Prediction and Internal Statistical Cross Validation

Forecasting types and assessment approaches

Overfitting

Internal Statistical Cross-validation is an iterative process

Example (Linear Regression)

Cross-validation methods

Case-Studies

Summary of CS output

Alternative predictor functions

Prediction Models

Appendix: R Debugging

XXI. Function Optimization

Free (unconstrained) optimization

Constrained Optimization

Equality nand Inequality constraints

Lagrange Multipliers

Linear and Quadratic Programming

Manual vs. Automated Lagrange Multiplier Optimization

Data Denoising

XXII. Deep Learning