Lecture Schedule
This is a tentative schedule. It is subject to change.
Format (flipped classroom): before each content session, watch the pre-recorded lecture and complete the knowledge-check questions (due before class starts). In class (105 min): a ≤30-minute recap with cold-call Q&A, then an extended small-group activity. Quiz days are quiz-only — 60 minutes allotted for a ~40-minute quiz, and you are dismissed afterward — except quiz-3 day, which continues into the course-synthesis class exercise. Friday discussion sections are reserved for Spark project team meetings; attendance is mandatory. Midterm and final project orals also happen in your discussion section.
Key dates: Quizzes in class Wed Oct 7, Mon Nov 2, and Wed Dec 2 (paper and pencil, no devices, closed notes; a reference sheet covering each quiz’s whole unit is published one week ahead and handed out printed on the day). The team midterm project runs Oct 7 – Sun Nov 1 with orals Fri Nov 6; the team final project runs Nov 9 – Wed Dec 2 with orals Fri Dec 4.
| # | Date | Session | Pre-recorded lecture | In-class activity |
|---|---|---|---|---|
| Week 1 | — | — | — | — |
| 1 | Wed Sep 2 | Course intro: format, grading, AI-assisted workflow | — (first day) | Environment setup; Git + Python warm-up |
| Week 2 | — | — | — | — |
| — | Mon Sep 7 | 🍂 No class — Labor Day 🍂 | ||
| 2 | Wed Sep 9 | ⚡ Spark pitches (~30 min) ⚡ + What is Data Science? | What is Data Science? | Pandas tutorial |
| Week 3 | — | — | — | — |
| 3 | Mon Sep 14 | Distances and similarity | Distances + linear-algebra essentials | Scikit-Learn tutorial; distance matrices on real data |
| 4 | Wed Sep 16 | Clustering I: k-means | k-means | k-means from scratch; choosing k |
| Week 4 | — | — | — | — |
| 5 | Mon Sep 21 | Clustering II: hierarchical | Hierarchical clustering | Dendrograms; messy-data failure modes |
| 6 | Wed Sep 23 | Probabilistic modeling | Probabilistic modeling | Fit distributions to real data |
| Week 5 | — | — | — | — |
| 7 | Mon Sep 28 | Clustering III: GMM and EM | GMM & EM | EM on a 1-D mixture |
| 8 | Wed Sep 30 | Generalization | Generalization | Learning curves; train/test discipline |
| Week 6 | — | — | — | — |
| 9 | Mon Oct 5 | Classification I: trees and random forests | Decision trees & forests | Tree building; feature-importance pitfalls |
| 10 | Wed Oct 7 | 📝 Quiz 1 (60 min; dismissed after) | — | 🔬 Team midterm project assigned |
| Week 7 | — | — | — | — |
| — | Mon Oct 12 | 🍂 No class — Indigenous Peoples’ Day 🍂 | ||
| 11 | Tue Oct 13 (Monday schedule) | Classification II: k-NN (+ Naive Bayes highlights) | k-NN | Classifier bake-off; curse of dimensionality |
| 12 | Wed Oct 14 | Regression I: linear | Linear regression | Least squares by hand; residual diagnostics |
| Week 8 | — | — | — | — |
| 13 | Mon Oct 19 | Regression II: logistic + regularization | Logistic & regularization | Regularization paths; class imbalance |
| 14 | Wed Oct 21 | SVD and low-rank approximation | SVD | Image compression; latent structure |
| Week 9 | — | — | — | — |
| 15 | Mon Oct 26 | Dimensionality reduction: PCA and t-SNE | PCA & t-SNE | PCA on real data; t-SNE sensitivity |
| 16 | Wed Oct 28 | Causal inference I | Causal I | Confounding scenarios; simulate a confounder |
| Week 10 | — | — | — | — |
| 17 | Mon Nov 2 | 📝 Quiz 2 (60 min; dismissed after) | — | 🎤 Midterm due Sun Nov 1; team orals Fri Nov 6 in section |
| 18 | Wed Nov 4 | Causal inference II | Causal II | Backdoor identification; adjust and estimate |
| Week 11 | — | — | — | — |
| 19 | Mon Nov 9 | Neural networks I: how learning works | Neural networks I | Gradient descent + backprop by hand. 🔬 Team final project assigned |
| 20 | Wed Nov 11 | Neural networks II: making training work | Neural networks II | Train, overfit, and repair an MLP |
| Week 12 | — | — | — | — |
| 21 | Mon Nov 16 | Intro to NLP | NLP (packages reference) | Text pipeline; TF-IDF similarity |
| 22 | Wed Nov 18 | Recommender systems | Recommenders | Matrix factorization; cold start |
| Week 13 | — | — | — | — |
| 23 | Mon Nov 23 | Graphs I | Networks I | Graphs in NetworkX |
| — | Wed Nov 25 | 🦃 No class — Thanksgiving 🦃 | ||
| Week 14 | — | — | — | — |
| 24 | Mon Nov 30 | Graphs II: centrality and communities | Networks II | PageRank; centrality disagreements |
| 25 | Wed Dec 2 | 📝 Quiz 3 (60 min) + synthesis exercise | — | Techniques-map revisit. 🔬 Team final project due; orals Fri Dec 4 |
| Week 15 | — | — | — | — |
| 26 | Mon Dec 7 | 📽️ Spark presentations I 📽️ | ||
| 27 | Wed Dec 9 | 📽️ Spark presentations II 📽️ |
Optional modules (recorded, not scheduled): Time series, Anomaly detection (slides), RNNs, CNNs, and the reference appendices.
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