Type of Credit: Elective
Credit(s)
Number of Students
This comprehensive course on Natural Language Processing (NLP) is designed to equip students with Python skills and knowledge to handle text data effectively. The course covers a broad range of NLP tasks, from basic text handling to advanced topics. The curriculum includes crucial concepts such as text data manipulation, regular expressions, tokenization, stemming, lemmatization, vocabulary matching, part of speech tagging, named entity recognition, and text classification. The course further explores advanced topics such as Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), word vectors, and deep learning techniques such as Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformers. Students will also get to learn about cutting-edge technologies such as OpenAI's Language Model (LLM). By the end of the course, students will be equipped with the skills to handle complex NLP challenges and create their own AI-driven applications.
能力項目說明
By the end of this course, students will have gained the following skills:
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
|
週次 |
課程主題 |
課程內容與指定閱讀 |
教學活動與作業 |
|
1 |
Introduction |
Introduction to Natural Language Processing |
|
|
2 |
NLP Basics |
Python with Text |
|
|
3 |
NLP Basics |
Regular Expressions |
|
|
4 |
NLP Basics |
Text Preprocessing |
|
|
5 |
NLP Basics |
Part of Speech |
|
|
6 |
NLP Basics |
Named Entity Recognition |
Assignment 1 |
|
7 |
NLP Basics |
Semantics & Sentiment Analysis |
|
|
8 |
NLP in Machine Learning |
Text Classification |
Assignment 2 |
|
9 |
NLP in Machine Learning |
Latent Dirichlet Allocation & Non-negative Matrix Factorization |
Assignment 3 |
|
10 |
NLP in Deep Learning |
Recurrent Neural Network |
|
|
11 |
NLP in Deep Learning |
LSTMs and GRU |
|
|
12 |
NLP in Deep Learning |
Text Generation |
Assignment 4 |
|
13 |
NLP in Deep Learning |
Transformers |
|
|
14 |
LLM |
Open AI’s LLM |
|
|
15 |
LLM |
Open AI’s LLM |
Assignment 5 |
|
16 |
Presentation |
Final Project Presentation |
|
Assignments 30%
Final Project 50%
Class Participation 20%Natural Language Processing with Python
--- Analyzing Text with the Natural Language Toolkit
https://www.nltk.org/book_1ed/
Speech and Language Processing
https://www.nltk.org/book_1ed/ https://web.stanford.edu/~jurafsky/slp3/