Lecture 1 - Introduction to the Course Lecture 2 - Text Processing Basics, Tokenization Lecture 3 - N-gram Language Models - Part 1 Lecture 4 - N-gram Language Models - Part 2 Lecture 5 - NLP Tasks and Paradigms Lecture 6 - Tutorial 1 Lecture 7 - Supervised Learning Lecture 8 - Shallow Neural Networks Lecture 9 - Deep Neural Networks Lecture 10 - Backpropagation Lecture 11 - Gradient Descent and Initialization Lecture 12 - Tutorial 2 Lecture 13 - Word Representation Lecture 14 - Learning Word Representation - Part I Lecture 15 - Learning Word Representation - Part II Lecture 16 - Word Vectors: Other Extensions Lecture 17 - Cross-Lingual Representations Lecture 18 - Tutorial 3 Lecture 19 - RNN Language Models Lecture 20 - RNN Applications : Text Generation, Sequence Labeling, Text Classification Lecture 21 - RNN for Sequence to Sequence Lecture 22 - Decoding Strategies Lecture 23 - Better RNN Units : GRU, LSTM Lecture 24 - Tutorial 4 Lecture 25 - Introduction to Transformers Lecture 26 - Transformers - Part 2 Lecture 27 - Transformers - Part 3 Lecture 28 - Transformers - Part 4 Lecture 29 - Efficient Transformers Lecture 30 - Tutorial 5 Lecture 31 - Pretraining Lecture 32 - Pretraining Transformer Encoder Lecture 33 - Pretraining Transformer Encoder, Encoder-Decoder Lecture 34 - Pretraining Transformer Decoder Lecture 35 - More on Pretraining Lecture 36 - Tutorial 6 Lecture 37 - Applications : Question Answering - I Lecture 38 - Applications : Question Answering - II Lecture 39 - Applications : Dialogue Systems - I Lecture 40 - Applications : Dialogue Systems - II Lecture 41 - Applications : Text Summarization Lecture 42 - Tutorial 7 Lecture 43 - Instruction Fine-Tuning - I Lecture 44 - Instruction Fine-Tuning - II Lecture 45 - Reinforcement Learning from Human Feedback - I Lecture 46 - Reinforcement Learning from Human Feedback - II Lecture 47 - Aligning to User Preferences via Direct Preference Optimization Lecture 48 - Tutorial 8 Lecture 49 - Prompting - I Lecture 50 - Prompting : Why does in-context learning work? Lecture 51 - Advanced Prompting Techniques Lecture 52 - Tool-aided Language Models Lecture 53 - Automatic Prompt Engineering Lecture 54 - Tutorial 9 Lecture 55 - Parameter-efficient fine-tuning - I Lecture 56 - Parameter-efficient fine-tuning - II Lecture 57 - Efficient fine-tuning for quantized LMs - I Lecture 58 - Efficient fine-tuning for quantized LMs - II Lecture 59 - Other Parameter Efficient Methods: Pruning, Distillation Lecture 60 - Tutorial 10 Lecture 61 - Scaling Laws of LLMs Lecture 62 - Modern LLMs and Architecture Variations - I Lecture 63 - Modern LLMs and Architecture Variations: Positional Embeddings Lecture 64 - Long Sequence Modeling Lecture 65 - Retrieval Augmented Generation Lecture 66 - Tutorial 11 Lecture 67 - Model Interpretability Lecture 68 - Model Interpretability - Multilingual Lecture 69 - Model Interpretability - III Lecture 70 - Trustworty LLMs : Taxonomy Lecture 71 - Trustworty LLMs : Machine Unlearning Lecture 72 - Tutorial 12