Syllabus

Course information

Course BIOST 2155 — Introductory Statistical Learning for Health Sciences
Meeting day/time Fridays, 9:00–10:50 AM
Location 2121C Public Health
Credit hours 2
Term Fall 2026

Instructor

Name Soumik Purkayastha, PhD, Assistant Professor of Biostatistics and Health Data Science
Email soumik@pitt.edu
Phone (412) 624-0336
Office A740 Public Health
Office hours Fridays, 2:00–3:00 PM (or by appointment)

Course description

This 2-credit course introduces basic concepts and methods for statistical learning, with an emphasis on modern health science applications. Topics include linear regression with regularization, supervised classification, unsupervised clustering, and dimensionality reduction. The target audience is second-year Biostatistics master’s students or early PhD students with interests in statistical learning techniques for health science data. Students will be trained with hands-on materials to understand the methods, implement the algorithms, and interpret results in real applications.

Prerequisites

BIOST 2141, 2142, and 3011. Students are expected to have programming experience in R, or in a low-level language such as C, C++, Java, or Fortran. Prerequisites can be met with other relevant coursework or experience with consent from the instructor.

Learning objectives

By the end of this course, students will be able to:

  1. Explain the motivation and intuition behind the statistical learning methods covered in class.
  2. Demonstrate basic theory proofs for selected fundamental concepts and methods.
  3. Implement methods on real datasets in health sciences.
  4. Produce an analysis pipeline and interpret the results in a real application.

Textbook

Primary textbook (highly recommended; free PDF):

  • James, Witten, Hastie & Tibshirani. An Introduction to Statistical Learning: with Applications in R. Springer, 2013. https://www.statlearning.com/

Optional references:

  • Hastie, Tibshirani & Friedman. The Elements of Statistical Learning. Springer, 2009.
  • Prince. Understanding Deep Learning. MIT Press, 2024.
  • Bishop. Pattern Recognition and Machine Learning. Springer, 2006.

Computing

We will use R as our main programming language. RStudio with R Markdown is recommended for homework. Templates are provided.

Grading

Letter-grade cut-offs: A 90–100%, B 80–89%, C 70–79%, D 60–69%, F < 60%. Plus/minus assigned by thirds within each band.

Component Weight
Quizzes (12, lowest two dropped†, averaged) 10%
Homework 1 12%
Homework 2 12%
Homework 3 12%
Homework 4 12%
Homework 5 12%
Final project presentation (group) 20%
Final project report: final slide deck (group) 10%

† See quiz policy below.

Quiz policy

A short 5-question quiz follows each of the 12 lecture topics, released on Canvas the same day as lecture and due before the next lecture. Quizzes check basic comprehension of that week’s material and are graded on correctness. Your lowest two quiz scores are dropped before averaging, so a couple of missed or rough weeks don’t disproportionately affect your grade. No late quizzes — the drop policy exists instead of a late-submission exception.

Homework policy

Five homework assignments, each covering two consecutive lecture topics (modules 2–11) — a mix of theory, simulation, and applied data analysis in R. Each is scoped to about 5 hours of work, released once both of its covered lectures have been taught and due two weeks later. Multiple hypothesis testing (module 12) has no homework, only its quiz. Submissions are electronic via Canvas. Discussion of homework with classmates is allowed, but each student must independently write and implement their own solution. Solutions are graded on both correctness and clarity. Partial credit is given for identifying gaps in an argument when a complete solution cannot be derived.

Late homework: 80% credit if 0–1 days late, 50% credit if 1–2 days, 0% if more than 2 days. Exceptions for sickness or emergency require the instructor be informed in advance.

Final project

The final project is done in groups of 2–3. Each group submits a one-page proposal by 11/06 (required, not graded), gives a 15-minute presentation in class on 12/04, and submits a revised slide deck as its report by 12/09. Groups may analyze a real dataset or reproduce a published analysis. See the project page for details.

Academic integrity

Students in this course will be expected to comply with the University of Pittsburgh’s Policy on Academic Integrity. Any student suspected of violating this obligation for any reason during the semester will be required to participate in the procedural process, initiated at the instructor level, as outlined in the University Guidelines on Academic Integrity. To learn more about Academic Integrity, visit the Academic Integrity Guide for an overview of the topic.

For hands-on practice, complete the Academic Integrity Modules.

Use of generative AI

Intellectual integrity is vital to an academic community and for my fair evaluation of your work. All work completed and/or submitted in this course must be your own, completed in accordance with the University’s Guidelines on Academic Integrity. You may not engage in unauthorized collaboration or make use of ChatGPT or any other generative AI applications at any time.

Disability services

The University of Pittsburgh is committed to inclusive and accessible education experiences for all students. If you have a disability for which you are, or may be, requesting an accommodation, you are encouraged to contact Disability Resources and Services (DRS), 140 William Pitt Union, (412) 648-7890, drsrecep@pitt.edu, as early as possible in the term. DRS will engage in a review process with you to determine eligibility and reasonable accommodations. If you are already registered with DRS, please complete the necessary steps on the DRS Student Portal to utilize your accommodations this term.

Accessibility

The University of Pittsburgh is committed to providing equal access by making all electronic and information technology accessible to all users. This includes instructional materials and the Canvas LMS platform.

  • If you experience accessibility issues regarding course-specific material, please reach out to the instructor.
  • If you experience a University-wide or other large-scale accessibility issue, please report the digital accessibility barrier to the Digital Accessibility Coordinator.
  • If you are registered with Disability Resources and Services and experience issues with approved accommodations, you may contact Disability Resources and Services (DRS), 140 William Pitt Union, 412-648-7890, drsrecep@pitt.edu.

Religious observances

The University of Pittsburgh strives to foster a supportive environment and to respect the beliefs and values of all community members. Accordingly, recognizing that certain students may observe religious holidays (activities observed by a religious group of which a student is a member) and cultural practices that conflict with scheduled class activities, instructors will work with students to try to make reasonable accommodations in the event of such a conflict.

At the beginning of the semester, you should review the course requirements to identify foreseeable conflicts with assignments, exams, or other required attendance. If you identify a conflict, please contact your instructor (or your course coordinator/s), ideally within the first two weeks of the first class meeting, to allow time to discuss and attempt to make fair and reasonable adjustments to the schedule and/or tasks.

Classroom recording

To ensure the free and open discussion of ideas, students may not record classroom lectures, discussion and/or activities without the advance written permission of the instructor, and any such recording properly approved in advance can be used solely for the student’s own private use.

Title IX & sexual misconduct

University faculty are required to report instances of sexual misconduct, including harassment and sexual violence, to the Office of Civil Rights and Title IX. To make a confidential report, the University Counseling Center is available at 412-648-7930 (8:30 AM – 5 PM M–F) and 412-648-7856 (after hours). Pittsburgh Action Against Rape: 1-866-363-7273 (24/7). For an immediate safety concern, contact the University of Pittsburgh Police: 412-624-2121.

An exception to the reporting requirement applies to disclosures shared as a relevant part of an academic project, classroom discussion, or course assignment.

Student wellness

Your well-being plays an important role in your ability to learn, engage, and succeed academically, and seeking support when needed is a normal and healthy part of the academic experience. Visit the Student Wellness resources site to learn more about available resources, schedule appointments, and connect with support services. Mental health support is also available 24/7 through the University Counseling Center at 412-648-7930. You can also call or text 988 to reach the Suicide & Crisis Lifeline, or text HOME to 741741 to connect with the Crisis Text Line. If there is a risk of harm to yourself or others, contact University of Pittsburgh Police at 412-624-2121 or dial 911.

Statement provided by the Office of the Provost, April 17, 2026.

Class meeting in event of building closures

It is the policy of the University of Pittsburgh to remain open other than during University holidays. In rare instances, circumstances beyond the University’s control may necessitate a closure of University buildings. Such circumstances may include severe weather, power outage, water main issues, and the like.

If feasible during such a closure, I intend for our class to meet remotely at its regularly scheduled time. Refer to our class attendance policy if you are unable to attend.

Health Sciences Library

Recommended for research-intensive courses: the Health Sciences Library and the school’s dedicated librarian, Candise Branum, are available for research support.