Natual Language Processing (CS438)

Course Overview

Natural Language Processing course offers a comprehensive overview of how machines understand and interpret human language, diving into the realms of machine learning, deep learning, and their applications in NLP. Ideal for students with a foundational understanding of AI and programming, this course promises to equip you with the skills and knowledge to navigate and contribute to the rapidly evolving field of NLP.

Our journey through NLP will encompass both foundational theories and practical applications. Students will be introduced to the basics of language processing, including syntax, semantics, and pragmatics, and how these aspects are interpreted by AI. As the course progresses, we will delve into more advanced topics like machine learning algorithms, deep learning techniques, and their applications in NLP.  

Lecture Schedule

Monday: 14:00 - 14:50

Wednesday: 15:00 -15:50

Friday: 16:00 - 16:50

Reference Texts

The following texts are useful, but none are required. 

• Dan Jurafsky and James H. Martin. Speech and Language Processing (3rd ed. draft)

• Jacob Eisenstein. Natural Language Processing

• Yoav Goldberg. A Primer on Neural Network Models for Natural Language Processing

• Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning

• Delip Rao and Brian McMahan. Natural Language Processing with PyTorch 

• Lewis Tunstall, Leandro von Werra, and Thomas Wolf. Natural Language Processing with Transformers

• James A.. Natural language Understanding 2e, Pearson Education

• Bharati A., Sangal R., Chaitanya V.. Natural language processing: a Paninian perspective

• Siddiqui T., Tiwary U. S. Natural language processing and Information retrieval

• Sowmaya Vajjala, Bodhisattwa Majumder, Anuj Gupta and Harshit Surana. Practical Natural Language Processing.