Schedule

Course Schedule

Day	Date	Topic	Videos	Notebook	Assessment	Materials
0	Pre-course	Launchpad: Welcome, course setup, and Colab orientation	2 videos		Colab Readiness Check	Google Colab docs
1	Mon May 18	Predictive analytics fundamentals, EDA, and data splitting	4 videos		Concept Quiz	ISLP Ch 2 sklearn: cross-validation Kaggle Learn: data leakage
2	Tue May 19	Data setup and preprocessing pipelines (the professional way)	3 videos		Concept Quiz Participation	sklearn: pipelines, ColumnTransformer Pedregosa et al.: scikit-learn paper
3	Wed May 20	Regression metrics and baseline modeling (with test-set lockbox discipline)	3 videos		Concept Quiz 3-sentence Evaluation Note	ISLP: Model Assessment sklearn: regression metrics
4	Thu May 21	Linear regression that actually works: features, interactions, diagnostics	3 videos		Concept Quiz Participation	ISLP Ch 3: Linear Regression sklearn: LinearRegression, PolynomialFeatures
5	Fri May 22	Regularization (Ridge/Lasso) + Project proposal sprint	Synchronous lecture + 3 videos		Concept Quiz PROJECT MILESTONE 1: Proposal + Dataset	ISLP Ch 6: Regularization sklearn: Ridge/Lasso/ElasticNet
6	Mon May 25	Logistic regression: probabilities, decision boundaries, and pipelines	3 videos		Concept Quiz Participation	ISLP Ch 4: Classification sklearn: LogisticRegression
7	Tue May 26	Classification metrics: confusion matrix, ROC and PR curves, and business costs	3 videos		Concept Quiz Threshold Recommendation	Fawcett: ROC analysis Saito & Rehmsmeier: PR curves sklearn: classification metrics
8	Wed May 27	Resampling and CV: how to compare models without fooling yourself (k-fold + Student’s t CIs)	3 videos		Concept Quiz Participation	ISLP Ch 5: Resampling sklearn: cross-validation utilities
9	Thu May 28	Hyperparameter tuning + feature engineering + leakage detection (and Project baseline build)	8 videos		Concept Quiz Project Baseline Draft	sklearn: GridSearchCV, RandomizedSearchCV Provost & Fawcett: evaluation framing
10	Fri May 29	Midterm: Business-case predictive strategy practicum + Project baseline submission	Synchronous lecture + 3 videos		MIDTERM (graded) PROJECT MILESTONE 2: Baseline Model + Evaluation Plan	Provost & Fawcett: business framing sklearn: common pitfalls
11	Mon Jun 1	Decision trees: interpretable models with sharp edges	6 videos		Concept Quiz Participation	ISLP Ch 8: Tree-Based Methods sklearn: DecisionTree estimators
12	Tue Jun 2	Random forests: bagging, OOB intuition, and feature importance	6 videos		Concept Quiz Participation	Breiman: Random Forests paper sklearn: RandomForest, permutation importance
13	Wed Jun 3	Gradient boosting: performance with discipline (and leakage avoidance)	6 videos		Concept Quiz Participation	Friedman: Gradient Boosting Machine sklearn: gradient boosting estimators
14	Thu Jun 4	Model selection and comparison: making the call like a professional	6 videos		Concept Quiz Participation	ISLP: Model Assessment sklearn: model evaluation best practices
15	Fri Jun 5	Final Project Milestone 03 Walkthrough — Complex Model + Hyperparameter Tuning + Draft Abstract	Synchronous lecture + 6 videos		Concept Quiz PROJECT MILESTONE 3: More Complex Model + Hyperparameter Tuning + Draft Abstract + saved champion_pipeline.joblib	M3 rubric: complex-model tuning, CI-overlap rule, draft abstract sklearn: GridSearchCV, joblib persistence
16	Mon Jun 8	Time-series forecasting: walk-forward CV, lag features, and baseline models	6 videos		Concept Quiz Participation	Hyndman & Athanasopoulos: FPP3 (otexts.com/fpp3) sklearn: TimeSeriesSplit
17	Tue Jun 9	Data communication and poster design: six principles (context, visualization, less-is-more, hierarchy, beauty, story) applied to the eleven-section research-poster architecture	6 videos		Concept Quiz Draft Poster Outline + Abstract	Tufte: data-ink ratio Healy: Data Visualization Knaflic: Storytelling with Data
18	Wed Jun 10	Competition workflow: end-to-end pipeline from notebook to Kaggle submission	6 videos		Concept Quiz submission.csv to Kaggle	Chip Huyen: Designing ML Systems sklearn: model persistence (joblib)
19	Thu Jun 11	Special topic: deep learning (awareness, when-to-use, and one tabular demo)	6 videos		Concept Quiz Four-Question Rubric	ISLP Ch. 10: Deep Learning Goodfellow et al.: Deep Learning Book
20	Fri Jun 12	Course end and reflection: project package submission + peer review + reflection survey	Synchronous lecture + 6 videos		PROJECT MILESTONE 4: Final Research Poster (single PDF named <group-number>.pdf) + intra-group Peer Evaluation form KAGGLE COMPETITION DEADLINE (11:59 PM) REFLECTION SURVEY (required for course completion)	Course rubric for M4 final poster Purdue URC poster guidelines

Core Course References

• James, Witten, Hastie, Tibshirani. An Introduction to Statistical Learning (ISLP) + Python labs. Download: https://www.statlearning.com/
• Hastie, Tibshirani, Friedman. The Elements of Statistical Learning (ESL).
• Provost, Fawcett. Data Science for Business.
• Pedregosa et al. “Scikit-learn: Machine Learning in Python.” JMLR.
• scikit-learn User Guide (pipelines, preprocessing, model selection, metrics, inspection).
• Chip Huyen. Designing Machine Learning Systems (deployment thinking, monitoring).