Natural Language Processing

Program Overview

This module offers an introduction to the current state-of-the-art techniques in the area of Natural Language Processing (NLP), namely Large Language Modules (LLMs). LLMs are among the defining technologies of our time. They form the core of modern Artificial Intelligence (AI) and are transforming how societies communicate and how knowledge is accessed.

Program Outcomes

This course introduces students to the foundations, architectures, and applications of modern language modeling. It covers both theoretical and practical aspects of LLMs, ranging from count-based and neural language models to Transformer architectures, alignment techniques, reasoning, retrieval-augmented generation (RAG), and multimodal extensions. A major emphasis is placed on developing ethical, transparent, and globally diverse models.

Learning Objectives

·        Explain the fundamental principles of language modeling, including count-based, neural, and transformer-based approaches. 

·        Implement small-scale language models and conduct experiments withPyTorch and Hugging Face. 

·        Develop and fine-tune specialized models, including reasoning models, multilingual models, and multimodal models that process text alongside images, audio, or video. 

·        Design and prototype basic AI agents that use LLMs to perform tasks such as question answering, planning, and interaction with external tools. 

·        Identify, analyze, and mitigate ethical risks such as bias, misinformation, and under-representation in model behavior and datasets.