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 for the midterm and final class projects: team work time and the project oral checkpoints. Attendance is mandatory on the oral Fridays (Oct 16, Oct 23, Nov 6, Nov 20, Dec 4).
Key dates: Quizzes in class Wed Oct 7, Mon Nov 2, and Wed Dec 2 and the final exam on Wed Dec 16 12:00 - 2:00 PM (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 oral checkpoints Fri Oct 16 and Fri Oct 23 and the final oral Fri Nov 6; the team final project runs Nov 9 – Wed Dec 2 with a written check Fri Nov 13, an oral checkpoint Fri Nov 20 and the final oral Fri Dec 4.
| # | Date | Topic | In-class activity | Assignments/Notes |
|---|---|---|---|---|
| Week 1 | — | — | — | — |
| 1 | Wed Sep 2 | Course intro: format, grading, AI-assisted workflow | Environment setup; Git + Python warm-up | |
| Week 2 | — | — | — | — |
| — | Mon Sep 7 | 🍂 No class — Labor Day 🍂 | 📄 HW1 (question types, vectors) released | |
| 2 | Wed Sep 9 | ⚡ Spark pitches (~30 min) ⚡ + What is Data Science?  |
Question types and Pandas tutorial (reference) | |
| Week 3 | — | — | — | — |
| 3 | Mon Sep 14 | Distances and similarity + linear-algebra essentials |
Scikit-Learn tutorial; distance matrices on real data (reference) | 📄 HW2 (distances) released |
| 4 | Wed Sep 16 | Clustering I: k-means |
k-means from scratch; choosing k | ✅ HW1 due |
| Week 4 | — | — | — | — |
| 5 | Mon Sep 21 | Clustering II: hierarchical |
Dendrograms; messy-data failure modes | ✅ HW2 due 📄 HW3 (k-means, linkage) released |
| 6 | Wed Sep 23 | Probabilistic modeling |
Fit distributions to real data | |
| Week 5 | — | — | — | — |
| 7 | Mon Sep 28 | Clustering III: GMM and EM |
EM on a 1-D mixture | ✅ HW3 due 📄 HW4 (probability, EM) released |
| 8 | Wed Sep 30 | Generalization |
Learning curves; train/test discipline | 📘 Quiz 1 reference sheet posted |
| Week 6 | — | — | — | — |
| 9 | Mon Oct 5 | Classification I: trees and random forests |
Case Study 08: Prioritizing Breast Biopsies with Trees and Forests | ✅ HW4 due Quiz 1 Review |
| 10 | Wed Oct 7 | 📝 Quiz 1 (60 min; dismissed after) | — | 🔬 Team midterm project released — due Sun Nov 1, orals Fri Nov 6 📄 HW5 (bias–variance, trees) released Thu Oct 8 |
| Week 7 | — | — | — | — |
| — | Mon Oct 12 | 🍂 No class — Indigenous Peoples’ Day 🍂 | ||
| 11 | Tue Oct 13 (Monday schedule) | Classification II: k-NN (+ Naive Bayes highlights) |
Case Study 09: Which Classifier Should the Consortium Buy? | ✅ HW5 due 📄 HW6 (k-NN, The Curse, Bayes) released |
| 12 | Wed Oct 14 | Regression I: linear |
Case Study 10: Predicting Diabetes Progression for a Care-Management Program | 🎤 Midterm checkpoint 1 Fri Oct 16 in section (push Thu Oct 15) |
| Week 8 | — | — | — | — |
| 13 | Mon Oct 19 | Regression II: logistic regression |
Case Study 11A: Setting the Approval Threshold for a Credit Union’s Small-Business Loans | ✅ HW6 due 📄 HW7 (least squares, logistic) released |
| 14 | Wed Oct 21 | Regression III: regularization |
Case Study 11B: Which Attributes Should the Loan Officers Check? | 🎤 Midterm checkpoint 2 Fri Oct 23 in section (push Thu Oct 22) |
| Week 9 | — | — | — | — |
| 15 | Mon Oct 26 | SVD and low-rank approximation lecture recording coming soon |
Case Study 12: How Much Can a Trail-Camera Network Compress Its Photos? | ✅ HW7 due 📘 Quiz 2 reference sheet posted |
| 16 | Wed Oct 28 | Dimensionality reduction: PCA and t-SNE lecture recording coming soon |
Case Study 13: Can Thirteen Lab Measurements Tell Wines Apart? | |
| Week 10 | — | — | — | — |
| 17 | Mon Nov 2 | 📝 Quiz 2 (60 min; dismissed after) | — | 🎤 Midterm project due Sun Nov 1 — team orals Fri Nov 6 in section 📄 HW8 (SVD, PCA) released Tue Nov 3 |
| 18 | Wed Nov 4 | Causal inference lecture recording coming soon |
Case Study 14: Did the Community Health Worker Program Reduce ER Visits? | |
| Week 11 | — | — | — | — |
| 19 | Mon Nov 9 | Neural networks I: how learning works lecture recording coming soon |
Case Study 16: The Model Stopped Learning After the Refactor | ✅ HW8 due 📄 HW9 (causal, backprop) released 🔬 Team final project released — due Wed Dec 2, orals Fri Dec 4 |
| 20 | Wed Nov 11 | Neural networks II: making training work lecture recording coming soon |
Case Study 17: Reading Handwritten Form Fields on a Fixed Training Budget | 📝 Final project written check: push Fri Nov 13 |
| Week 12 | — | — | — | — |
| 21 | Mon Nov 16 | Intro to NLP (packages reference) lecture recording coming soon |
Case Study 18: Routing and De-duplicating a Help Desk’s Tickets | ✅ HW9 due 📄 HW10 (TF-IDF, MF, graphs) released |
| 22 | Wed Nov 18 | Recommender systems lecture recording coming soon |
Case Study 19: Recommendations for a Public Library’s E-Media Platform | 🎤 Final checkpoint 1 Fri Nov 20 in section (push Thu Nov 19) |
| Week 13 | — | — | — | — |
| 23 | Mon Nov 23 | Graphs I lecture recording coming soon |
Case Study 20: Mapping a Volunteer Network for a Community Nonprofit | ✅ HW10 due |
| — | Wed Nov 25 | 🦃 No class — Thanksgiving 🦃 | 📘 Quiz 3 reference sheet posted | |
| Week 14 | — | — | — | — |
| 24 | Mon Nov 30 | Graphs II: centrality and communities lecture recording coming soon |
Case Study 21: Who Should Coordinate, and Where Would the Chapter Split? | |
| 25 | Wed Dec 2 | 📝 Quiz 3 (60 min) + synthesis exercise | Techniques-map revisit | 🎤 Final project due Wed Dec 2 — team orals Fri Dec 4 in section |
| Week 15 | — | — | — | — |
| 26 | Mon Dec 7 | 📽️ Spark presentations I 📽️ | ||
| 27 | Wed Dec 9 | 📽️ Spark presentations II 📽️ | ||
| Finals Week | — | — | — | — |
| 28 | Wed Dec 16 12:00 - 2:00 PM | 📝 3-Part Final Exam (120 min) | CDS Room 263 |
Optional modules (recorded, not scheduled): Time series, Anomaly detection (slides), Causal inference: estimating effects (notes only), RNNs, CNNs, and the reference appendices.
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