Instructor: Yifan Peng (yip4002@med.cornell.edu)
Time: Jan. 6, 2025 - April 11, 2025, 5:15-8:15 pm East Time on Mondays
Location: 575 Lex
TA: Zihan Xu (zix4004@med.cornell.edu)
Office Hours: TBD
Grading: Letter grade
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.
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.
- 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.
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
If you are interested in Deep Learning
| Week | Topic | Event | Deadline |
|---|---|---|---|
| 1/6 | Introduction | ||
| 1/13 | Text preprocessing and regular expression | Assignment 1 | |
| 1/20 | Martin Luther King, Jr. Day – no classes | ||
| 1/27 | n-gram | Assignment 2 | Assignment 1 |
| 2/3 | Text classification | ||
| 2/10 | Part-of-speech tagging and parsing | Assignment 3 | Assignment 2 |
| 2/17 | Presidents' Day - no classes | ||
| 2/24 | Word vector | Project proposal | |
| 3/3 | Intro to deep learning | Assignment 4 | Assignment 3 |
| 3/10 | CNN, RNN, and Transformer | ||
| 3/17 | Large Langauge Model | Assignment 5 | Assignment 4 |
| 3/24 | Prompt engineering and LLM fine-tuning | ||
| 3/31 | Multimodal large language models | Assignment 5 | |
| 4/7 | Final project presentation | Final project paper |