TAC-459: Generative AI & NLP

Instructor/Course Assistant	Mode	Day	Time Slot	Location	Meeting Link
Rajeev Singh	Online	Monday	11:00 AM - 1:00 PM	Zoom	Meeting Link
Reza Jafarkhani	Online + Offline (RRB 211)	Tuesday	2:30 PM - 3:30 PM	Zoom	Meeting Link
Allen Bolourchi	Online	Tuesday	7 PM - 8 PM	Zoom	Email Professor atleast 48 hours before to Schedule(15 minutes meet)
Rajeev Singh	Online	Wednesday	11:00 AM - 1:00 PM	Zoom	Meeting Link
Reza Jafarkhani	Online + Offline (RRB 211)	Thursday	2:30 PM - 3:30 PM	Zoom	Meeting Link
Rajeev Singh	Online	Friday	11:00 AM - 1:00 PM	Zoom	Meeting Link

Course Schedule

Week	Topics	Deliverables
Module 1: Introduction to Text Processing, NLP, and AI
1	Introduction to NLP, Generative AI, and Text Processing Expand topics Course Overview Historical Evolution of AI and NLP Key Concepts in Machine Learning and NLP Overview of NLP Applications in Various Domains Basic Text Processing and Language Understanding Challenges and Limitations in NLP	-
2	Text Cleaning and Preprocessing Expand topics Text Cleaning and Preprocessing Techniques for Noise Reduction Text Normalization and Tokenization Lemmatization and Stemming Co-occurrence Matrix in Text Analysis Feature Extraction from Text Regular Expressions in Text Processing Basic Overview of Word Embeddings Overview of python libraries such as NLTK, Spacy, regex etc.	Assignment 1 Due
Module 2: Fundamentals of Machine Learning for NLP
3	Fundamentals of Machine Learning for NLP, Metrics, and Cross-Validation Expand topics Supervised Learning and Unsupervised Learning - Key Algorithms for NLP Evaluation Metrics for NLP Models Training, Validation, and Test Sets in Model Development Overfitting and Underfitting in NLP Cross-Validation Techniques Introduction to Python ML Libraries	Assignment 2 Due
4	Neural Networks and Deep Learning in NLP Expand topics Neural Networks and Deep Learning in NLP Activation Functions and Network Topologies Backpropagation and Gradient Descent CNNs and RNNs for NLP Advanced RNNs: LSTM and GRU Sequence Modeling in NLP Challenges in Deep Learning for NLP Case Studies in Deep Learning for NLP	Assignment 3 Due
5	Transformer Models and Attention Mechanisms Expand topics Transformer Models and Attention Mechanisms Understanding the Transformer Architecture Concepts of Self-Attention and Positional Encoding Overview of BERT, GPT, and Transformer Variants Applications of Transformer Models in NLP Training Transformer Models for NLP Tasks Challenges and Solutions with Transformer Models	Assignment 4 Due
Module 3: NLP Techniques and Applications
6	Syntax, Parsing, Word Embeddings, and POS Tagging Expand topics Syntax, Parsing, Word Embeddings, and POS Tagging Syntax in Natural Language Processing Dependency and Constituency Parsing Deep Dive into Word Embeddings Word2Vec, GloVe, and FastText Embeddings Using Embeddings in NLP Tasks Part-of-Speech (POS) Tagging: Importance, Methods, and Tools Practical Parsing, Embedding, and POS Tagging Techniques Vector Database	Assignment 5 Due
7	Semantic Analysis, Language Models, and Question Answering Expand topics Semantic Analysis, Language Models, and Question Answering Semantic Role Labeling (SRL) Knowledge Graphs in Semantic Analysis Advances in Contextual Embeddings Overview of Language Models in NLP Introduction to Question Answering System and Utilizing Language Models Challenges in QA and Semantic Analysis Word Sense Disambiguation Techniques	Assignment 6 Due
8	Text Classification and Machine Translation + Midterm Exam Expand topics MidTerm Exams!! Text Classification and Machine Translation Fundamentals of Text Classification Techniques and Algorithms for Classification Introduction to Machine Translation Neural Machine Translation (NMT) models Challenges and Evaluation Metrics for MT Practical Implementation of MT Systems	-
Module 4: Specialized Topics and Advanced Techniques in NLP
9	Advanced Topics in Machine Learning and NLP Expand topics Advanced Topics in Machine Learning and NLP Deep Transfer Learning in NLP Strategies for Addressing Data Imbalance Model Interpretability and Explainability in NLP Advanced Optimization Techniques in NLP Utilizing Pre-Trained NLP Models Case Studies of Advanced ML in NLP	Assignment 7 Due
10	Named Entity Recognition, Information Retrieval and Search Expand topics Named Entity Recognition, Information Retrieval and Search Advanced Techniques in NER Contextual NER and Its Applications Fundamentals of Information Retrieval Deep Dive into TF-IDF and Co-occurrence Matrix Search Engines and Indexing Techniques Evaluation Metrics in Information Retrieval Case Studies and Real-World Applications	Assignment 8 Due
11	Advanced Machine Translation and Summarization Expand topics Advanced Machine Translation and Summarization Advanced Techniques in Machine Translation Handling Low-Resource Languages in MT Text Summarization Extractive vs. Abstractive Summarization Challenges in Summarization Evaluation of Summarization Techniques Current Trends in MT and Summarization	Assignment 9 Due
12	Speech Processing and Conversational AI Expand topics Speech Processing and Conversational AI Basics of Speech Recognition Challenges in Automatic Speech Recognition (ASR) Design and Development of Conversational Agents Evaluating Dialogue Systems in Conversational AI Multimodal Interaction in Conversational AI Natural Language Understanding in Conversational AI Case Studies in Speech Processing and Conversational AI	Assignment 10 Due
Module 5: Generative AI
13	Generative AI, Products, Techniques, APIs, and Ethical Considerations Expand topics Generative AI, Products, Techniques, APIs, and Ethical Considerations Generative Models in NLP (ChatGPT, BARD, Gemini, Llama) Advanced Prompt Engineering: Few-shot, Chain-of-thought, and Self-Consistency Expanding LLM Capabilities: Knowledge Generation Prompting and Program-aided (PAL) Developing an API with MongoDB and ngrok, and interfacing via Postman LLM Safety: Prompt Injection, Prompt Leaking, Jailbreaking Responsible AI, Data Privacy, Ethical Considerations, and Governance in AI Business Intelligence, Marketing, Analytics, and Brand Analysis Use Cases Introduction to Product Management and Business Considerations	Assignment 11 Due
14	Training and Fine-Tuning LLMs, Hugging Face, LangChain, and RAG Expand topics Training and Fine-Tuning LLMs, Hugging Face, LangChain, and RAG RAG: Retrieval Augmented Generation Training of Chat GPT with Reinforcement Learning, HITL, and Proximal Policy Optimization Evaluation of LLMs: MMLU, HellaSwag Benchmark, ARC, WinoGrade, GSM-8k, Truthful QA, PIQA LoRA and QLoRA for Efficient Model Adaptation Fine Tuning LLMs, Best Practices and Challenges Fine-Tuned LLMs Application Overview and Practical Use of Hugging Face in NLP Based Products Utilizing LangChain and Llama for Customized Language Models	Assignment 12 Due
15	Course Review, Future Trends, and Project Presentations + Final Exam Expand topics Course Review, Future Trends, and Project Presentations + Final Exam Recap of Key NLP and Generative AI Concepts Discussion on the Future Trends in NLP and Generative AI Student Project Presentations Feedback and Review of Projects Resources for Advanced Learning Closing Remarks and Course Evaluation	Final Project Deliverables Due

TAC-459: Generative AI and Natural Language Processing

Course Overview

Instructors

Prof. Allen Bolourchi

Prof. Reza Jafarkhani

Course Assistants

Rajeev Singh

Nitin Sai Bommi

Varuna Krishna Kolla

Office Hours

Course Schedule