Course Syllabus


John Curtin,

Office hours: Mondays, 10 – 11 am (Rm 326) or by appointment

Teaching Assistants

Gaylen Fronk ()

  • Office hours: Fridays 2:30 - 3:30 pm (Rm 325) or by appointment

Sarah Sant’Ana ()

  • Office hours: Mondays 1:30 - 2:30 pm (Rm 325) or by appointment

Class Email Listserv


We will respond to all email communications within 1 business day (and often much quicker). Please plan accordingly (e.g., weekend emails may not receive a response until Monday). For general questions about class, homework, etc., please post the question to the class email listserv. If you have the question, you are probably not alone. For issues relevant only to you (e.g., class absences, accommodations, etc.), email John. John may share the email with the TAs unless you request otherwise.

Meeting Times

Tuesdays and Thursdays from 1:00 - 2:15 pm in Rm 228

Course Description

This course is designed to introduce students to a variety of computational approaches in machine learning. The course is designed with two key foci. First, students will focus on the application of common, “out-of-the-box” statistical learning algorithms that have good performance and are implemented in the caret package in R. Second, students will focus on the application of these approaches in the context of common questions in behavioral science in academia and industry.


Students are required to have completed Psychology 610 with a grade of B or better.

Learning Outcomes

  • Students will develop and refine best practices for data wrangling, general programming, and analysis in R.

  • Students will distinguish among a variety of learning settings: supervised learning vs. unsupervised learning, regression vs. classification

  • Students will be able to implement a broad toolbox of well-supported machine-learning methods: decision trees, nearest neighbor, general and generalized linear models, penalized models including ridge, lasso, and elastic-nets, neural nets, support vector machines

  • Students will develop expertise with common feature extraction techniques for quantitative and categorical predictors.

  • Students will be able to use natural language processing approaches to extract meaningful features from text data.

  • Students will know how to characterize how well their regression and classification models perform and they will employ appropriate methodology for evaluating their: cross validation, ROC and PR curves, hypothesis testing.

  • Students will learn to apply their skills to common learning problems in psychology and behavioral sciences more generally.

Course Topics

  • Overview of Machine Learning Concepts and Uses
  • Data wrangling in R within the Tidyverse
  • Iterations, functions, simulations in R
  • Introduction to regression models
  • Introduction to classification models
  • Model performance metrics
  • ROCs
  • Cross validation and other resampling methods
  • Model selection and regularization
  • Approaches to parallel processing
  • Feature engineering techniques
  • Natural language processing
  • Tree based methods
  • Bagging and boosting
  • Support vector machines
  • Neural networks
  • Dimensionality reduction and feature selection
  • Ethics and privacy issues

Required Textbooks and Software

All required and reference textbooks are freely available online (though hard copies can also be purchased if desired). The six textbooks that we will focus on for the course include:

  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning: With Applications in R (7th ed.). (pdf)

  • Kuhn, M. & Johnson, K. (2018). Applied Predictive Modeling. New York, NYL Springer Science. (pdf)

  • Grolemund, G., & Wickham, H. (2016). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (1st ed.). Sebastopol, CA: O’Reilly Media, Inc. (bookdown)

  • Silge, J., & Robinson, D. (2017). Text Mining with R: A Tidy Approach (1rst ed.). Beijing; Boston: O’Reilly Media. (bookdown)

  • Kuhn, M., & Johnson, K. (2019). Feature Engineering and Selection: A Practical Approach for Predictive Models (1 edition). Boca Raton, FL: Chapman and Hall/CRC. (bookdown)

  • Wickham, H. (2019). The Tidy Style Guide. (bookdown)

Additional articles will be assigned and provided by pdf through the course website.

All data processing and analysis will be accomplished using R. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.


  • Homework assignments (13 anticipated): 20%
  • Reading quizzes: 10%
  • Midterm exam/project: 35%
  • Final exam/project: 35%

Final letter grades may be curved upward, but a minimum guarantee is made of an A for 93 or above, AB for 88 - 92, B for 83 - 87, BC for 78 - 82, C for 70 - 77, D for 60-69, and F for < 60.

Projects and Quizzes

The midterm project will be assigned as a take-home examine near the mid-point of the course (date TBD).

The final project will be assigned as a take-home exam due Friday, May 8th at 5 pm.

Approximately weekly reading quizzes will be conducted through Canvas at the start of class on either Tuesdays or Thursdays.


The approximately weekly programming assignments are due on Tuesdays at 9 am through Canvas. These assignments are to be done individually. You may communicate with other class members about the problem, but please do not share answers or code. You are also encouraged to make use of online resources (e.g., stack overflow) for assistance. All assignments will be completed using R markdown to provide both the code and documentation as might be provided to your mentor or employer to fully describe your solution. Your code should run locally in R with no modifications other than the installation of necessary R packages. Late homework is not accepted because problem solutions are provided that same day in class. Grades for each homework will be posted by Friday of the same week.


Class topics and reading assignments are described on the course website. Class meets on Tuesdays and Thursdays from 1 – 2:15 pm unless otherwise indicated.

Student Ethics

The members of the faculty of the Department of Psychology at UW-Madison uphold the highest ethical standards of teaching and research. They expect their students to uphold the same standards of ethical conduct. By registering for this course, you are implicitly agreeing to conduct yourself with the utmost integrity throughout the semester.

In the Department of Psychology, acts of academic misconduct are taken very seriously. Such acts diminish the educational experience for all involved – students who commit the acts, classmates who would never consider engaging in such behaviors, and instructors. Academic misconduct includes, but is not limited to, cheating on assignments and exams, stealing exams, sabotaging the work of classmates, submitting fraudulent data, plagiarizing the work of classmates or published and/or online sources, acquiring previously written papers and submitting them (altered or unaltered) for course assignments, collaborating with classmates when such collaboration is not authorized, and assisting fellow students in acts of misconduct. Students who have knowledge that classmates have engaged in academic misconduct should report this to the instructor.


Occasionally, a student may have a complaint about a TA or course instructor. If that happens, you should feel free to discuss the matter directly with the TA or instructor. If the complaint is about the TA and you do not feel comfortable discussing it with him or her, you should discuss it with the course instructor. Complaints about mistakes in grading should be resolved with the TA and/or instructor in the great majority of cases. If the complaint is about the instructor (other than ordinary grading questions) and you do not feel comfortable discussing it with him or her, make an appointment to speak to the Associate Chair for Graduate Studies, Professor Kristin Shutts, .

If your complaint concerns sexual harassment, you may also take your complaint to Dr. Linnea Burk, Clinical Associate Professor and Director, Psychology Research and Training Clinic, Room 315 Psychology (262-9079; ).

If you have concerns about climate or bias in this class, or if you wish to report an incident of bias or hate that has occurred in class, you may contact the Chair of the Department, Professor Craig Berridge () or the Chair of the Psychology Department Climate & Diversity Committee, Professor Martha Alibali (). You may also use the University’s bias incident reporting system.

Diversity and Inclusion

Diversity is a source of strength, creativity, and innovation for UW-Madison. We value the contributions of each person and respect the profound ways their identity, culture, background, experience, status, abilities, and opinion enrich the university community. We commit ourselves to the pursuit of excellence in teaching, research, outreach, and diversity as inextricably linked goals.

The University of Wisconsin-Madison fulfills its public mission by creating a welcoming and inclusive community for people from every background – people who as students, faculty, and staff serve Wisconsin and the world.

Accommodations Policy

The University of Wisconsin-Madison supports the right of all enrolled students to a full and equal educational opportunity. The Americans with Disabilities Act (ADA), Wisconsin State Statute (36.12), and UW-Madison policy (Faculty Document 1071) require that students with disabilities be reasonably accommodated in instruction and campus life. Reasonable accommodations for students with disabilities is a shared faculty and student responsibility. Students are expected to inform faculty [me] of their need for instructional accommodations by the end of the third week of the semester, or as soon as possible after a disability has been incurred or recognized. Faculty [I], will work either directly with the student [you] or in coordination with the McBurney Center to identify and provide reasonable instructional accommodations. Disability information, including instructional accommodations, as part of a student’s educational record is confidential and protected under FERPA.