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README.md

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+## HINF 5016 Natural Language Processing in Health
+
+**Spring, 2025**
+
+https://bionlplab.github.io/2025_Spring_NLP_in_Health/

_config.yml

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+title: "HINF 5016: Natural Language Processing in Health"
+description: "Jan. 10, 2024 - April 10, 2024, 5:15-8:15 pm East Time on Wednesdays | 
+              WCMC Campus; 1300 Auditorium; A-950"
+theme: jekyll-theme-cayman

index.md

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+**Instructor**: Yifan Peng ([yip4002@med.cornell.edu](yip4002@med.cornell.edu))<br>
+**TA**: TBD<br>
+**Office Hours**: TBD<br>
+**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 is 14 weeks in length. The course follows the progression of topics: python review, regular expression and automata, text normalization, n-gram, text classification, sequence labeling, parsing, word vector, introduction to deep learning, convolutional neural network and recurrent neural network, and transformer-based method. Each topic is addressed in a module lasting 1-2 weeks. Students will work on an individual project in parallel with these activities and give a final presentation last week.
+
+### 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](https://link.springer.com/book/10.1007/978-3-031-55865-8)
+* [Natural Language Processing with Python](https://www.nltk.org/book/)
+* [Foundations of Statistical Natural Language Processing](https://nlp.stanford.edu/fsnlp/)
+* [Speech and Language Processing (3rd ed. draft)](https://web.stanford.edu/~jurafsky/slp3/)
+* [Natural Language Processing](https://github.com/jacobeisenstein/gt-nlp-class/blob/master/notes/eisenstein-nlp-notes.pdf)
+
+If you are not very familiar with Python
+
+* [Python for Data Science](https://github.com/jakevdp/PythonDataScienceHandbook)
+* [A byte of Python](https://python.swaroopch.com/)
+
+If you are interested in Deep Learning
+
+* [Deep Learning](http://www.deeplearningbook.org/)
+* [Natural Language Processing with PyTorch](https://www.amazon.com/Natural-Language-Processing-PyTorch-Applications/dp/1491978236)
+
+### Tentative Course Schedule Overview 
+
+| Week | Topic | Event | Deadline |
+|----|-------------------------------------------|-----------------------------|---------------------|
+| 1 | Introduction | | |
+| 2 | Regular expression | Assignment 1 | |
+| 3 | Text preprocessing | | |
+| 4 | n-gram | Assignment 2 | Assignment 1 |
+| 5 | Text classification | |  |
+| 6 | Sequence labeling | Literature review | Assignment 2 |
+| 7 | Parsing | | |
+| 8 | Word vector | Project proposal | |
+| 9 | Intro to deep learning | | |
+| 10 | CNN and RNN | Assignment 3 | Literature review |
+| 11 | Transformer | | |
+| 12 | Large Langauge Model | Assignment 4 | Assignment 3 |
+| 13 | NLP applications in Healthcare | | |
+| 14 | Final project presentation | | Assignment 4 |
+| 15 | - | | Final project paper |