Textbooks:

Syllabus:

Part I – Foundations 

• Data Preparation & Tokenization, [slides] [notebook, in-class lab]

• N-Gram Language Models [slides]  

• Text Classification approaches [slides] 

• Intro to Neural Networks [slides]

• Vector Semantics & Embeddings [slides] 

• Transformers [slides]  

• Text Clustering & Topic Modeling [slides]  

Part II – Modern NLP

• Fine-Tuning Deep Dive [slides]

• Retrieval-Augmented Generation, promoting & LLM Agents [slides]

• Evaluation metrics [slides]

• Deployment & Optimization 

Part III – Speech Integration