Instructor: Yifan Peng (yip4002@med.cornell.edu)
Time: Jan. 12, 2026 - April 13, 2026, 5:00-8:00 pm East Time on Mondays
Location: TBD
TA: Haotian Ma ham7026@med.cornell.edu, Geoffrey Martin ghm4002@med.cornell.edu
Office Hours: TBD
Grading: Letter grade

Course Aims and Outcomes

This course provides students with an understanding of the field of natural language processing and its applications in health. Students will acquire knowledge of sources of text data, linguistic structures, and the range of methods available for processing. Hands-on experience with the Python programming language and tool kit will provide useful skills for managing text data for solving a variety of problems in the health domain.

Format and Procedures

The course follows the progression of topics: text preprocessing and regular expression, n-gram, text classification, sequence labeling, parsing, word vector, convolutional neural network and recurrent neural network, and transformer-based methods. Each topic is addressed in a module lasting 1-2 weeks. Students will work on individual assignments alongside these activities, as well as participate in a team project.

Prerequisites

• Python: Prior exposure to programming and Python is highly recommended. We will provide a tutorial on Python in the first two weeks.
• Basic Probability and Statistics: You should know the basics of probabilities, mean, standard deviation, etc.
• College Calculus, Linear Algebra: You should understand matrix/vector notation and operations.

Reference Texts

The following texts are useful, but none are required.

• Natural Language Processing in Biomedicine
• Natural Language Processing with Python
• Foundations of Statistical Natural Language Processing
• Speech and Language Processing (3rd ed. draft)
• Natural Language Processing

If you are not very familiar with Python

• Python for Data Science
• A byte of Python

If you are interested in Deep Learning

• Deep Learning
• Natural Language Processing with PyTorch

Tentative Course Schedule Overview

Date	Week	TOPICS	READINGS OR PRE-WORK DUE BEFORE CLASS	ASSIGNMENTS DUE
1/12	1	Introduction
1/19	2	Martin Luther King, Jr. Day – no classes
1/26	3	Text preprocessing and regular expression	Homework 1
2/2	4	n-gram
2/9	5	Text classification	Homework 2	Homework 1
2/16	6	Presidents’ Day - no classes
2/23	7	Part-of-speech tagging and parsing	Homework 3	Homework 2
3/2	8	Word vector
3/9	9	Intro to deep learning	Homework 4	Homework 3
3/16	10	CNN, RNN, and Transformer
3/23	11	Large Language Model	Homework 5	Homework 4
3/30	12	Multimodal large language models
4/6	13	Final project presentation		Homework 5
4/13		Final Exams		Final project manuscript