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Learn fundamental and advanced concepts in natural language processing through this comprehensive university course covering machine learning foundations, neural networks, transformers, and modern language models. Master essential NLP techniques including tokenization, vector semantics, embeddings, and language modeling while exploring neural machine translation and attention mechanisms. Dive deep into transformer architectures for both encoding and decoding, understanding pretraining and fine-tuning processes with hands-on demonstrations using DeBERTa and sequence-to-sequence models. Examine reinforcement learning from human feedback (RLHF) and instruction fine-tuning approaches alongside taskification and benchmarking methodologies. Study traditional NLP tasks such as part-of-speech tagging, named entity recognition, and hidden Markov models with parameter estimation and efficient inference techniques. Explore syntactic analysis through constituency and dependency parsing, semantic parsing, and semantic role labeling. Investigate coreference resolution, probing techniques, and question answering systems while addressing multilingual and multimodal large language models. Conclude with critical topics in responsible AI, summarization techniques, and comprehensive review sessions to solidify understanding of the complete NLP landscape.
