Machine Learning: Theory and Applications

Our goal is to provide a clear and accessible “behind the scenes” understanding of many machine learning procedures for prediction and classification that are more frequently being used in the health and behavioral sciences. We strive to strike a balance between core concepts of the algorithmic procedures along with their application and interpretation when implemented with real empirical data. This workshop is designed to provide participants with the knowledge to discern which machine learning models are appropriate for their research question and the subsequent tools to analyze their data in ways not previously possible.

This workshop is designed for graduate students, post-doctoral fellows, faculty, and research scientists from the behavioral, social, and health sciences. Although it is recommended that participants have a working knowledge of the general regression model, these core concepts will be reviewed at the beginning of the workshop. Participants who seek a more in depth review prior to attending may wish to watch our Linear Regression Playlist on YouTube.

Chapter 1: General Introduction
1.1 Introduction to the Predictive Modeling Process
1.2 Matrix Algebra Review
1.3 Data Pre-processing
1.4 Principal Component Analysis

Chapter 2: Over-Fitting and Model Tuning
2.1 The Problem of Over-Fitting
2.2 Model Tuning and Data Splitting
2.3 Resampling Techniques
2.4 Choosing Between Models

Chapter 3: Regression Models
3.1 Variance-Bias Trade-Off
3.2 Linear Regression
3.3 Partial Least Squares
3.4 Penalized Models/Regularization
3.5 Multivariate Adaptive Regression Splines

Chapter 4: Regression Trees
4.1 Regression Tree Models
4.2 Rule-Based Models
4.3 Model Aggregation: Bagging/Boosting
4.4 Random Forests

Chapter 5: Linear Classification Models
5.1 Evaluating Classification Models
5.2 Logistic Regression
5.3 Linear Discriminant Analysis and Variants
5.4 Nearest Shrunken Centroids

Chapter 6: Nonlinear Classification Models
6.1 Nonlinear Discriminant Analysis
6.2 Flexible Discriminant Analysis
6.3 Support vector machines

Chapter 7: Classification Trees
7.1 Classification Tree Models
7.2 Model Aggregation: Bagging/Boosting
7.3 Random Forests
7.4 C5.0 Algorithm

Chapter 8: Additional Considerations
8.1 Addressing Class Imbalance
8.2 Variable (Feature) Importance and Selection
8.3 Type III Errors (rejecting the null for the wrong reasons)
8.4 Measurement Error
8.5 Trusting Your Model

Pre-recorded software demonstrations for machine learning are provided for self-paced learning using R and Python. Note that both R and Python can be downloaded for free (R download; Python download) While it is helpful to have some familiarity with R or Python, it is not necessary. The lectures which constitute the majority of the workshop are software-independent.

Machine Learning: Theory and Applications is a five-day workshop originally taught live via Zoom by Doug Steinley. Daily lectures were held from 10:00 to 3:30 with morning, lunch, and afternoon breaks. Sessions consisted of comprehensive lectures, detailed presentation of real-data examples, response to participant questions, and general discussion. Pre-recorded software demonstration videos are also available in R and Python.

Self-paced participants receive lifetime access to all course materials, including complete course notes, lecture recordings, software demonstration notes, software demonstration recordings, and data and code for all examples. You can revisit these materials any time you like, without worrying about expiration dates. Pretty awesome, huh?