Lecture 1 - Introduction and Recent Advances Lecture 2 - Introduction to Natural Language Processing Lecture 3 - Introduction to Statistical Language Models Lecture 4 - Statistical LM: Advanced Smoothing and Evaluation Lecture 5 - Introduction to Deep Learning Lecture 6 - Introduction to PyTorch Lecture 7 - Word Representation: Word2Vec and fastText Lecture 8 - Word Representation: GloVe Lecture 9 - Tokenization Strategies Lecture 10 - Neural Language Models: CNN and RNN Lecture 11 - Neural Language Models: LSTM and GRU Lecture 12 - Sequence-to-Sequence Models Lecture 13 - Decoding Strategies Lecture 14 - Attention in Sequence-to-Sequence Models Lecture 15 - Introduction to Transformer: Self and Multi-Head Attention Lecture 16 - Introduction to Transformer: Positional Encoding and Layer Normalization Lecture 17 - Implementation of Transformer using PyTorch Lecture 18 - Pre-Training Strategies: ELMo, BERT Lecture 19 - Pre-Training Strategies: Encoder-decoder and Decoder-only Models Lecture 20 - Introduction to Hugging Face Lecture 21 - Instruction Tuning Lecture 22 - Prompt-based Learning Lecture 23 - Advanced Prompting and Prompt Sensitivity Lecture 24 - Alignment of Language Models - I Lecture 25 - Alignment of Language Models - II Lecture 26 - Knowledge and Retrieval: Knowledge Graph Lecture 27 - Knowledge and Retrieval: Knowledge Graph Completion and Evaluation Lecture 28 - Knowledge and Retrieval: Translation and Rotation Models Lecture 29 - Parameter Efficient Fine-Tuning (PEFT) Lecture 30 - Quantization, Pruning and Distillation Lecture 31 - An Alternate Formulation of Transformers: Residual Stream Perspective Lecture 32 - Interpretability Techniques Lecture 33 - Knowledge and Retrieval: Multiplicative models Lecture 34 - Knowledge and Retrieval: Modeling Hierarchies Lecture 35 - Knowledge and Retrieval: Temporal Knowledge Graphs Lecture 36 - Responsible LLMs Lecture 37 - Conclusion: Expert Panel Discussion

NOC:Introduction to Large Language Models (LLMs)