Day-to-day content for Foundations of Machine Learning (DS3001)/Analytics 1 (DS3021)
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2026/01/13: Introduction
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2026/01/15: Wrangling
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2026/01/20: Wrangling, EDA
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2026/01/22: EDA
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2026/01/27: ECDFs and Outliers
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2026/01/29: k-NN classification
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2026/02/03: k-NN regression
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2026/02/05: Train-test splits, hyperparameters
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2026/02/10: NHANES Project
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2026/02/12: Linear Models
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2026/02/17: Feature Spaces
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2026/02/19: Bias-Variance Trade-Off
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2026/02/24: Cross Validation (and BVT)
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2026/02/26: Regularization (and CV and BVT)
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2026/03/05: Spring Break
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2026/03/07: Spring Break
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2026/03/10: Genomics Project
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2026/03/12: General Linear Models
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2026/03/17: General Linear Models
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2026/03/19: Trees
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2026/03/24: Ensembles
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2026/03/26: Random Forests
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2026/03/31: Classifier Evaluation
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2026/04/02: k-Means Clustering
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2026/04/07: Hierarchical Custering
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2026/04/09: Principal Components Analysis
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2026/04/14: Principal Components Analysis
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2026/04/21: Principal Components Regression
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2026/04/23: SciKit Pipelines
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2026/04/28: Demand Estimation Project