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Move all assignment due dates from Fridays to following Mondays:
- Assignment 1: Jan 16 → Jan 19
- Assignment 2: Jan 23 → Jan 26
- Assignment 3: Jan 30 → Feb 2
- Assignment 4: Feb 6 → Feb 9
- Assignment 5: Feb 13 → Feb 16
Updated in:
- slides/README.md, slides/week1/lecture3.md/.html/.pdf
- admin/syllabus.md/.pdf, admin/canvas-content.md, admin/github-tutorial.md
- syllabus/index.html, assignments/README.md, assignments/index.html
- All 5 assignment instruction pages (assignments/assignment-X/index.html)
- All 5 submodule READMEs and notebooks
Copy file name to clipboardExpand all lines: admin/canvas-content.md
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@@ -59,7 +59,7 @@ All assignments are submitted via GitHub Classroom. Each assignment page has an
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### Assignment 1: ELIZA Chatbot
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**Due: January 16, 11:59 PM EST | Weight: 15%**
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**Due: January 19, 11:59 PM EST | Weight: 15%**
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Build a pattern-matching chatbot based on Weizenbaum's classic ELIZA program (1966). You will implement string manipulation and regular expressions to create a conversational agent that simulates a Rogerian psychotherapist.
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### Assignment 2: SPAM Classifier
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**Due: January 23, 11:59 PM EST | Weight: 15%**
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**Due: January 26, 11:59 PM EST | Weight: 15%**
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Develop a text classification system to identify spam messages. You will explore different feature engineering approaches, implement tokenization strategies, and evaluate classifier performance using standard metrics.
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### Assignment 3: Wikipedia Embeddings
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**Due: January 30, 11:59 PM EST | Weight: 15%**
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**Due: February 2, 11:59 PM EST | Weight: 15%**
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Compare different text embedding methods (LSA, Word2Vec, BERT) on Wikipedia articles. You will visualize semantic relationships in embedding space and evaluate how well different methods capture meaning.
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### Assignment 4: Customer Service Chatbot
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**Due: February 6, 11:59 PM EST | Weight: 15%**
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**Due: February 9, 11:59 PM EST | Weight: 15%**
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Create a context-aware customer service chatbot using transformer-based models. You will implement retrieval-augmented generation to answer questions based on a knowledge base.
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### Assignment 5: Build GPT
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**Due: February 13, 11:59 PM EST | Weight: 15%**
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**Due: February 16, 11:59 PM EST | Weight: 15%**
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Implement and train a small GPT model from scratch. You will build the transformer architecture piece by piece, including self-attention, positional encoding, and autoregressive text generation.
<li><strong>Runtime</strong>: The notebook must run from start to finish without errors in a fresh Colab session</li>
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<li><strong>Dependencies</strong>: Include all imports and installations in the notebook</li>
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<li><strong>Data</strong>: The <code>instructions.txt</code> file should be loaded in your notebook (code for doing this is in the template notebook provided with the assignment)</li>
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<li><strong>Output</strong>: Keep cell outputs visible in your submission</li>
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<li><strong>Deadline</strong>: January 16, 2026 at 11:59PM EST</li>
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<li><strong>Deadline</strong>: January 19, 2026 at 11:59 PM EST</li>
<pstyle="margin-top: 1rem; font-size: 0.9rem; color: var(--text-secondary);">Due: January 30, 2026 at 11:59 PM EST</p>
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<pstyle="margin-top: 1rem; font-size: 0.9rem; color: var(--text-secondary);">Due: February 2, 2026 at 11:59 PM EST</p>
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</div>
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<h1id="assignment-3-representing-meaning---a-computational-exploration-of-semantic-space">Assignment 3: Representing Meaning - A Computational Exploration of Semantic Space</h1>
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