Lecture 1
Aug 25, 2026
Introduction & Course Overview
Read the notes for this lecture:
- What Can We Do with Language Models? — Start Here
- Assignments, Grades & Course Details — Start Here
Lecture 2
Aug 27, 2026
n-gram Language Models
Read the notes for this lecture:
- What Can We Do with Language Models? — Start Here
- Assignments, Grades & Course Details — Start Here
- How Language Models Assign Probabilities — Predicting Words
- Predicting the Next Word with n-grams — Predicting Words
- How Good Are the Predictions? Perplexity — Predicting Words
- Generating Text and Handling Unseen Words — Predicting Words
Lecture 3
Sep 1, 2026
n-grams & Smoothing + Logistic Regression
Read the notes for this lecture:
- Assignments, Grades & Course Details — Start Here
- How Language Models Assign Probabilities — Predicting Words
- Predicting the Next Word with n-grams — Predicting Words
- How Good Are the Predictions? Perplexity — Predicting Words
- Generating Text and Handling Unseen Words — Predicting Words
- Leaving Room for Unseen Word Pairs — Predicting Words
- Turning Text into Inputs a Model Can Use — Learning to Classify Text
- Making a Two-Class Prediction — Learning to Classify Text
Lecture 4
Sep 3, 2026
Logistic Regression (cont.)
Read the notes for this lecture:
- Assignments, Grades & Course Details — Start Here
- Leaving Room for Unseen Word Pairs — Predicting Words
- Turning Text into Inputs a Model Can Use — Learning to Classify Text
- Making a Two-Class Prediction — Learning to Classify Text
- How a Model Learns from Its Mistakes — Learning to Classify Text
- Helping a Model Work on New Examples — Learning to Classify Text
Lecture 5
Sep 8, 2026
Multinomial Logistic Regression & Word Embeddings
Read the notes for this lecture:
- Assignments, Grades & Course Details — Start Here
- Making a Two-Class Prediction — Learning to Classify Text
- How a Model Learns from Its Mistakes — Learning to Classify Text
- Helping a Model Work on New Examples — Learning to Classify Text
- Choosing among Several Classes with Softmax — Learning to Classify Text
- Representing Word Meaning with Vectors — Representing Word Meaning
Lecture 6
Sep 10, 2026
Word Embeddings (cont.)
Read the notes for this lecture:
- Assignments, Grades & Course Details — Start Here
- Choosing among Several Classes with Softmax — Learning to Classify Text
- Representing Word Meaning with Vectors — Representing Word Meaning
- Building Word Vectors from Counts — Representing Word Meaning
Lecture 7
Sep 15, 2026
Dense Word Embeddings & Feed-forward Neural Nets
Read the notes for this lecture:
- Assignments, Grades & Course Details — Start Here
- Representing Word Meaning with Vectors — Representing Word Meaning
- Building Word Vectors from Counts — Representing Word Meaning
- Learning Word Vectors by Prediction: word2vec — Representing Word Meaning
- GloVe, Similarity, and What Word Vectors Pick Up — Representing Word Meaning
- From Logistic Regression to Neural Networks — Neural Networks
Readings by lecture (Jurafsky & Martin, 3rd ed.): Lec 2 → Chapter 3 (n-grams). Lec 3 → Chapters 3 and 5. Lec 4 → Chapter 5 (logistic regression). Lec 5–6 → Chapter 6 (vector semantics and embeddings). Lec 6 also assigns word2vec Explained. Lec 7 → Chapter 7 (neural networks). See the course schedule for the reading links.