Course Materials

Material status
Required
Optional

# Module 1

Getting Started: Google Colab and GitHub

Learning objectives: Open, run, and save a notebook in Google Colab; navigate a GitHub repository and open a notebook from it; describe the course workflow for accessing, editing, and retaining copies of code.

Before class

During class

# Module 2

Tokens and Embeddings

Learning objectives: Tokenize short texts and interpret token IDs; use sentence embeddings to compare semantic similarity; distinguish representation models from text-generation models; explain how next-token probabilities produce generated text.

# Module 3

Text Classification I: Encoder Models

Learning objectives: Distinguish binary, multiclass, and multilabel classification problems; apply task-specific and embedding-based classifiers; evaluate predictions using suitable metrics and structured error analysis.

# Module 4

Text Classification II: Zero-Shot and Few-Shot Models

Learning objectives: Compare NLI-based zero-shot classification with prompted generative classification; use label descriptions, few-shot examples, and constrained outputs; test how label wording and examples affect classification performance.

Before class

# Module 5

Text Clustering and Topic Modeling

Learning objectives: Create document embeddings for an unlabeled text collection; cluster and visualize semantically related documents; interpret topic representations while identifying instability, labeling choices, and other limitations.

Before class

  • Chapter 5: “Text Clustering and Topic Modeling”Optional

# Module 6

Context Design, Model Benchmarks, and Evaluation

Learning objectives: Construct prompts with clear instructions, examples, context, and output constraints; distinguish model benchmarks from application and system evaluations; compare deterministic checks, rubrics, pairwise judgments, and human review; explain limitations involving prompt sensitivity, contamination, saturation, judge bias, latency, and cost.

During class

  • Mini evaluation lab: define grading criteria, compare two model or prompt conditions, and inspect disagreements among deterministic checks, rubric judgments, and human reviewRequired

# Module 7

RAG I: Dense Retrieval and Grounded Generation

Learning objectives: Chunk and embed a small document collection; build a dense semantic-retrieval workflow; use retrieved passages to produce grounded answers with source citations; distinguish retrieval failures from unsupported generation.

Before class

During class

# Module 8

RAG II: Hybrid Retrieval, Reranking, and Evaluation

Learning objectives: Compare lexical, dense, and hybrid retrieval; use a cross-encoder to rerank candidate passages; evaluate retrieval and answer quality; explain tradeoffs involving accuracy, latency, complexity, and cost.

During class

  • Guided lab: compare dense retrieval with a lexical or hybrid baseline, rerank candidates, and measure retrieval and grounded-answer qualityRequired

# Module 9

LLM Workflows, Tool Use, and Agentic RAG

Learning objectives: Distinguish deterministic workflows from model-directed agents; trace the tool-call, execution, observation, and response loop; explain how context, permissions, stopping conditions, and retrieval-as-a-tool affect reliability; identify failures from a supplied execution trace.

During class

# Module 10

Model Adaptation I: Text Embedding Models

Learning objectives: Explain when a general embedding model needs task or domain adaptation; organize query-positive pairs and hard negatives for contrastive learning; fine-tune a small embedding model; compare the original and adapted models on held-out retrieval examples.

Before class

During class

# Module 11

Model Adaptation II: Parameter-Efficient Fine-Tuning

Learning objectives: Choose among prompting, retrieval, embedding adaptation, and generative fine-tuning; prepare instruction or conversational data with the correct chat template; explain LoRA and QLoRA; compare a base and adapted model while assessing overfitting, privacy, computing, and maintenance risks.

Before class

During class

# Module 12

Project Presentations

Learning objectives: Present the project question, data, workflow, evaluation, and results as a coherent analytical story; use clear visuals or examples to explain model behavior; discuss limitations and responsible use and respond thoughtfully to questions and peer feedback.

Before class

  • Confirm that your GitHub repository opens correctly, the README identifies the starting point, and the analysis can be demonstrated during questions.Required
  • Submit the required presentation materials through the designated Canvas link before the presentation window closes.Required