This class is a great subject! Teaching “Mastering NLP from Foundations to LLMs” offers a broad and exciting curriculum. To help you structure your course, let’s break down potential topics and approaches:
Course Structure Suggestions:
- Module 1: Foundations of NLP:
- Introduction to Natural Language Processing (NLP) and its applications.
- Core concepts: text preprocessing (tokenization, stemming, lemmatization), n-grams, regular expressions.
- Basic NLP tasks: text classification, named entity recognition (NER), part-of-speech (POS) tagging.
- Introduction to different NLP libraries (NLTK, spaCy, Stanford CoreNLP).
- Module 2: Classical NLP Techniques:
- Feature engineering for text data (TF-IDF, word embeddings – Word2Vec, GloVe).
- Machine learning models for NLP: Naive Bayes, Support Vector Machines (SVMs), Logistic Regression, and their applications in various NLP tasks.
- Evaluation metrics for NLP tasks (precision, recall, F1-score, accuracy).
- Module 3: Deep Learning for NLP:
- Introduction to neural networks and their application to text data.
- Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs) for sequence modeling.
- Attention mechanisms and transformers.
- Pre-trained language models (e.g., BERT, RoBERTa, XLNet). Fine-tuning pre-trained models for specific tasks.
- Module 4: Large Language Models (LLMs):
- Architecture and training of LLMs.
- Capabilities and limitations of LLMs.
- Applications of LLMs (text generation, summarization, question answering, chatbots).
- Ethical considerations and responsible use of LLMs.
- Prompt engineering techniques.
- Module 5: Advanced Topics (Optional, depending on course level and time):
- Specific NLP applications (e.g., machine translation, sentiment analysis, information retrieval).
- Dialogue systems and chatbots.
- Knowledge graphs and their integration with NLP.
- NLP for specific domains (e.g., biomedical NLP, legal NLP).
Teaching Approaches:
- Lectures: Provide a structured overview of the concepts.
- Hands-on exercises and assignments: Students implement algorithms and work with real-world datasets. This is crucial for practical understanding.
- Projects: Larger-scale projects allow students to apply their knowledge to a complex problem.
- Readings: Provide supplementary materials for deeper understanding.
- Discussions: Encourage class participation and critical thinking.
Resources:
Consider incorporating resources like:
- Online courses (Coursera, edX, fast.ai)
- Research papers
- Open-source libraries (NLTK, spaCy)
- Datasets (e.g., IMDB movie reviews, GLUE benchmark)
Remember to tailor the course content and difficulty to the background and skill level of your students. Good luck!
