Unit 1: Fundamentals of NLP
Introduction to Natural Language Processing: Scope, challenges, and real-world applications. Linguistic Essentials: Morphology, syntax, semantics, pragmatics, and their relevance to NLP tasks. Text Processing Techniques: Tokenization, stemming, lemmatization, POS tagging, chunking, and dependency parsing. Traditional LanguageModeling: N-grams, smoothing techniques, basic probabilistic approaches. Vector Representations: Bag-of-Words, TF-IDF, distributional semantics, introduction to embeddings (Word2Vec, GloVe).
Unit 2: Deep Learning for NLP
Neural Architectures: RNNs, LSTMs, GRUs for sequence modeling, sequence-to-sequence frameworks.
Transformers and Attention Mechanisms: Self-attention, encoder-decoder frameworks, and the Transformer
architecture. Large Language Models (LLMs): BERT, GPT, T5, and other pretrained models; fine-tuning techniques
and transfer learning. Evaluation Metrics and Model Improvement: BLEU, ROUGE, METEOR, perplexity,
accuracy; model interpretability and error analysis. Introduction to Prompt Engineering: Harnessing pretrained
models through prompts and in-context learning.
Unit 3: Advanced NLP Techniques
Information Extraction and Semantic Understanding: Advanced NER, relation extraction, event detection,
coreference resolution, semantic role labeling. Document-Level NLP: Topic modeling (LDA), advanced text
classification, sentiment analysis, and stance detection. Few-shot and Zero-shot Learning: Exploiting pretrained
models to perform tasks with minimal task-specific data. Advanced Prompting and Instruction-Tuning:
Instruction-based interfaces (e.g., InstructGPT), guiding model behavior through task descriptions,
Chain-of-Thought prompting for improved reasoning. Optimization and Model Efficiency: Model compression,
quantization, distillation, and adapting LLMs for on-device and real-time NLP tasks.
Unit 4: Multimodal NLP and Ethics in NLP
Multi-modal NLP: Integrating text with images, video, and audio (CLIP, PaLM-E etc), bridging language with visual and other sensory modalities. Reinforcement Learning for Alignment: Reinforcement Learning from Human
Feedback (RLHF) to refine model behavior, reduce harmful outputs, and improve user alignment.Ethical and
Responsible AI in NLP: Bias detection and mitigation, fairness in language models, handling misinformation, and
differential privacy techniques.