Loading Courses

All Courses

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.
Tickets are no longer available

IN PERSON – 5-day Statistics Short Course

Data Mining and Machine Learning Seminar Overview:

An intermediate 5-day course introducing several popular machine learning approaches such as regression based methods (ridge and lasso regularized regression, regression splines), tree methods (random forests, boosted trees), support vector machines, and Interpretative Machine Learning (ILM) as well as their application to empirical data. The course combines lectures and hands-on practice using R.

Seminar Topics:

  • Review of linear regression and the least squares criterion
  • Regularization methods (ridge regression, lasso, elastic net)
  • Regression splines
  • Prediction error and k-fold cross validation
  • Tree methods to predict categorical or continuous outcomes (CART, random forest, boosting
  • Interpretative Machine Learning (IML)
  • Support vector machines for classification

Seminar Description:

Machine learning refers to leveraging data to build statistical models or algorithms. The objective is usually to gain knowledge about the structure in the data in order to make predictions or decisions.

This short course is based on

  • An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani)
  • Hands-on Machine Learning with R (Boemke & Greenwell)
  • Interpretable Machine Learning (Molnar)

The course starts with briefly outlining the key differences and similarities between standard parametric modeling (e.g., linear regression, structural equation modeling) and machine learning (aka statistical learning, aka data mining). The course provides basic insights into a number of popular methods such as regression methods (ridge regression and the lasso, regression splines), tree methods (CART, random forests, boosting), interpretable machine learning (IML), and support vector machines. The emphasis is on a conceptual understanding of these methods and their appropriate application to empirical data. Importantly, these methods are useful not only for large data collections, but also more generally for exploratory analyses when the substantive theory to design and fit parametric models (e.g. SEM) is lacking. Machine learning is used in a wide variety of fields including but not limited to public health, education, biology, and the different social sciences.

Participants are invited to discuss potential machine learning applications to their data during individual consultations with the instructor scheduled at the end of days 2-5.

Participants will receive an electronic copy of all course materials, including lecture slides, practice datasets, software scripts (R), relevant supporting documentation, and recommended readings. Participants will also have access to a video recording of the course.

 

Instructor: Gitta Lubke, Ph.D.

Gitta Lubke is a Professor Emerita in the Department of Psychology/Quantitative Area at the University of Notre Dame. Her research interests included machine learning and general latent variable modeling. Empirical applications were mainly in the field of psychiatric disorders and behavioral genetics. Other areas of expertise include mixture models, twin models, multi-group factor analysis and measurement invariance, longitudinal analyses, and the analysis of categorical data.

APA Continuing Education Credits:

data mining and machine learning

This course offers 29 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content.

Seminar Includes:

Materials, downloads, recorded course video viewable for up to one year.

Learning Objectives:

After engaging in course lectures and discussions as well as completing the hands-on practice activities with real data, participants will be able to:

  • Understand some of the key differences and similarities between parametric modeling and machine learning methods.
  • Expand the acquired basic knowledge of several popular machine learning methods and apply these methods to empirical data.
  • Implement ridge regression and Lasso.
  • Assess and interpret the results of empirical analyses through k-fold cross validation and computation of prediction errors.
  • Implement and evaluate regression splines.
  • Implement and evaluate decision trees to categorize data.
  • Utilize CART and bagging techniques.
  • Implement random forests to evaluate data.
  • Implement and evaluate boosted trees.
  • Understand several basic interpretable machine learning methods
  • Understand and utilize support vector machines
  • Utilize R packages for machine learning.
  • Understand and evaluate scientific papers covering basic machine learning applications to empirical data.

Seminar Prerequisites:

Required:

  • Advanced proficiency in linear regression, including the estimation of regression coefficients using least squares
  • Intermediate proficiency with R
  • Intermediate knowledge of exploratory data analysis
  • Basic familiarity with iterative optimization (e.g. Newton-Raphson algorithm to find a maximum)

Not required but advantageous:

  • Experience in calculus (e.g., graduate-level course)
  • Understanding the relation between multiple testing and Type I error

No level of proficiency beyond basic awareness is assumed for skills related to:

  • Machine learning methods
  • More advanced mathematical or statistical topics such as constrained estimation using Laplace multipliers

Software and Computer Support:

Participants need to bring a laptop computer with Wi-Fi capabilities.

All statistical software used at Stats Camp will be available, free to participants, on our SMORS (statistical modeling on remote servers) system for the duration of camp.

All instruction for this course will be based on the freely available software program R. Please make sure to have a recent version installed.

Seminar Audience:

Typically the ideal audience for this course in data mining and machine learning includes:

  1. Students pursuing a degree in computer science, engineering, statistics, or related fields who have a strong background in mathematics and programming.
  2. Researchers and professionals in the fields of data science, data analysis, artificial intelligence, and machine learning who want to learn new techniques and keep up with the latest developments in the field.
  3. Data analysts and data engineers who are interested in learning how to extract insights from large datasets using machine learning algorithms.
  4. Business professionals who are interested in understanding how data mining and machine learning can be applied to solve real-world business problems.
  5. Anyone who wants to gain a deeper understanding of the techniques and algorithms used in data mining and machine learning, and their applications in various fields.

The audience for a course in data mining and machine learning can be quite diverse, but typically consists of individuals with a strong background in quantitative analysis and a desire to apply machine learning techniques to real-world problems.

Course Learning Goals:

After engaging in course lectures and discussions as well as completing the hands-on practice activities with empirical data, participants will be able to:

  • Understand some of the key differences and similarities between parametric modeling and data mining methods
  • Expand the acquired basic knowledge of several popular data mining methods and apply these methods to empirical data
  • Assess and interpret the results of empirical analyses through k-fold cross validation and computation of prediction errors
  • Utilize R packages for data mining
  • Understand and evaluate scientific papers covering data mining applications to empirical data

Seminar Files

Seminar files will be provided by the instructor on the first day of the seminar. You do not need to download anything prior to the event date. All materials will be provided during or after the class.

All statistical software used at Stats Camp will be available, free to participants, on our SMORS (statistical modeling on remote servers) system for the duration of camp.

Monday June 12, 2023
9:00-9:30 Welcome and introductions
9:30-10:45 Simple and Multiple Linear Regression
10:45-11:00 Rest Break
11:00-12:30 Ridge Regression and Lasso
12:30-1:30 Rest Break
1:30-3:00 Prediction Error and Cross Validation
3:00-3:15 Rest Break
3:15-5:00 R lab: Ridge Regression and Lasso
Tuesday June 13, 2023
9:00-10:45 Regression Splines
10:45-11:00 Rest Break
11:00-12:30 R lab: Regression Splines
12:30-1:30 Rest Break
1:30-3:00 Introduction to Tree Methods
3:00-3:15 Rest Break
3:15-5:00 Individual consultation with instructor
Wednesday June 14, 2023
9:00-10:45 CART, bagging, Random Forests
10:45-11:00 Rest Break
11:00-12:30 R lab: Random Forests
12:30-1:30 Rest Break
1:30-3:00 Boosted Trees
3:00-3:15 Rest Break
3:15-5:00 Individual consultation with instructor
Thursday June 15, 2023
9:00-10:45 R lab: Boosted Trees
10:45-11:00 Rest Break
11:00-12:30 Interpretable Machine Learning (IML)
12:30-1:30 Rest Break
1:30-3:00 Deductive Data Mining
3:00-3:15 Rest Break
3:15-5:00 Individual consultation with instructor
Friday June 16, 2023
9:00-10:45 Support Vector Machines
10:45-11:00 Rest Break
11:00-12:30 R lab: Support Vector Machines
12:30-1:30 Rest Break
1:30-3:15 Individual consultation with instructor

Please fill out and submit the form below to get instant access to sample course materials.

Go to Top