CIS 113 - Natural Language Processing

Credit(s) 3.00
Lecture Hours: 3.00
This course introduces students to the theory and practice of Natural Language Processing (NLP). Students will learn the core concepts of text processing, tokenization, word embeddings, text classification, sentiment analysis, and sequence models, including recurrent neural networks (RNNs). The course integrates theoretical foundations with hands-on lab assignments using Python and popular NLP libraries. No

Prerequisite(s):

CIS 107

SLOs:
Upon successful completion of this course, students will be able to:

Explain the fundamental concepts of NLP and its applications.
Apply tokenization, normalization, and text preprocessing techniques.
Implement text classification and sentiment analysis models.
Use word embeddings (Word2Vec, GloVe, BERT) for semantic tasks.
Build and evaluate recurrent neural network (RNN) models for language tasks.
Integrate multiple NLP techniques into an end-to-end project.
Critically evaluate NLP models for accuracy, bias, and applicability.