Machine Learning for Classification Problems

Instructor: Doug Steinley
Software: R and Python
Lectures: 12 hours
Software Demonstrations: 1-2 hours per program
Evergreen Content: Continually updated
Lifetime Access: Materials never expire

Class Overview Video

Student: $599
Professional: $779

Savings Opportunity: Get more content and save up to $400 with our Machine Learning Bundle

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Category: Asynchronous, Workshops

Machine Learning for Classification Problems focuses on the application and interpretation of both traditional and next-generation machine learning (also statistical learning) approaches for classifying observations. One of the fastest growing areas of statistics and data analysis, machine learning applications have increased rapidly within the psychological, health, and social sciences. The methods covered in this workshop focus on producing robust classifications while achieving high accuracy (versus null hypothesis testing or statistical significance). When completed, participants will understand how to choose between machine learning techniques, utilizing software such as R and Python to compare a variety of classification models.

The workshop begins with common, fundamental models that are widely used, such as logistic regression, and uses these models to introduce and understand more advanced approaches such as classification trees and discriminant analysis. Along the way, a comprehensive approach to assessing model fit and protecting against overfitting will be developed. Finally, this workshop will also delve into the tension between “prediction” and “inference” and explores the implications for common research applications.

Note: Get lifetime access to both this class, focused on classifying observations to groups, and Machine Learning for Prediction, focused on prediction of continuous outcomes, at a significant savings of $400 off the professional rate (or $300 off the student rate) relative to the total cost when purchased separately with our Machine Learning Bundle. This gives you a total of 24 hours of pre-recorded lectures plus many more hours of pre-recorded demonstrations in R and Python.

1:46 Class Content / 11:49 Materials Provided / 14:33 Learning Objectives

Instructor

Doug Steinley, Ph.D.

Doug Steinley is a Professor in the Department of Psychology at the University of Missouri. His research and teaching focus on multivariate statistical methodology, with a primary interest in cluster analysis (both traditional procedures and more modern mixture modeling techniques) and social network analysis. Read More

Workshop Details

Goals of the Workshop

Our goal is to provide a clear and accessible “behind the scenes” understanding of many machine learning procedures for 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.

Who Should Attend

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 multiple 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.

Syllabus

Chapter 1: Introduction
1.1 Orientation of the Workshop
1.2 Goals of Supervised Classification
1.3 History and Evolution of Classification
1.4 Relationship to Predictive Modeling

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

Chapter 3: Linear Classification Models
3.1 Logistic Regression
3.2 Linear Discriminant Analysis and Variants
3.3 Other Methods of Linear Classification

Chapter 4: Nonlinear Classification Models
4.1 Nonlinear Discriminant Analysis
4.2 Flexible Discriminant Analysis
4.3 Support vector machines

Chapter 5: Classification Trees
5.1 Classification Tree Models
5.2 Model Aggregation: Bagging/Boosting
5.3 Random Forests
5.4 C5.0 Algorithm

Chapter 6: Additional Considerations
6.1 Addressing Class Imbalance
6.2 Variable (Feature) Importance and Selection
6.3 Type III Errors (rejecting the null for the wrong reasons)
6.4 Measurement Error
6.5 Trusting Your Model

Chapter 7: Next Steps
7.1 Classification through Self-Organized Maps and Neural Networks
7.2 Using Deep Learning for Classification
7.3 Semi-supervised Classification
7.4 Zero-shot Classification in Large Language Models

Software Considerations

What is Provided

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?

Content for this class is evergreen, meaning participants will always have access to the most current version of the course materials. Thus, if we replace a lecture recording or software demonstration, or update a syntax file, you will have immediate access to the updated content without needing to re-enroll in the class. Even more awesome.

Availability of Recordings

Certificate of Completion

After enrollment, you will have the option to generate a certificate of completion which will display your first and last name (as indicated in your user account), the name of the workshop, and the date upon which the certificate was generated. It can be printed to a PDF and will also be stored in the your user account for future access (after logging in to your account, select My Certificates from the user menu). Heck, you can even print out a hard copy and frame it to impress your friends.

Sample Content

Tuition Support for Students

We offer reduced-price registrations for undergraduate and graduate students who are actively enrolled in a recognized bachelor's, master's or doctoral training program. No application is necessary to qualify for the student tuition rates; simply choose the student rate when beginning the registration process at the top of the page. Confirmation of student status may be requested at a later time.

Reviews

Doug was a very engaging and easy to understand teacher. I loved his analogies and consistent themes of important grounding concepts (e.g. bias vs. variance).

I just liked Doug's attitude and excitement in covering the topics he did. It makes you want to learn more 🙂

Doug was very knowledgeable about the material. I liked how he could anticipate common questions and address these as part of the lecture.

I really liked the pre-recorded format of the lectures. That was really helpful. Also, this sounds silly, but Doug is really good at using the e-pen to draw on the slides and that was super helpful.

Doug was very energetic and very knowledgeable about the subject matter. Doug has a warm and charismatic personality, and even though statistics aren't my strong suit, I enjoyed this course! He made the material much more engaging, compelling, and interesting than I thought possible.

Doug was clear with his examples and was clearly knowledgeable on the topic. This class is theory-heavy, but he explained concepts and theoretical approaches with depth and clarity and was always open to additional questions. He clearly put effort into building students' knowledge and provided an open and non-judgmental environment for us.

Machine Learning for Classification Problems

Instructor

Doug Steinley, Ph.D.

Workshop Details

Reviews

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