Schedule

Week	Topic	Readings ISLP	Material*	Supplementary Materials
Week 1	Syllabus, Logistics, and Introduction.	Ch. 1; Ch. 2;	slides book lab	- Video: Statistical Learning: 2.1 Introduction to Regression Models - Video: Statistical Learning: 2.2 Dimensionality and Structured Models - Video: Statistical Learning: 2.3 Model Selection and Bias Variance Tradeoff - Video: Statistical Learning: 2.4 Classification - Video: Statistical Learning: 2.Py Data Types, Arrays, and Basics - 2023 - Video: Statistical Learning: 2.Py.3 Graphics - 2023 - Video: Statistical Learning: 2.Py Indexing and Dataframes - 2023
Week 2	Linear Regression	Ch. 3.	slides book lab	- Video: Statistical Learning: 3.1 Simple linear regression - Video: Statistical Learning: 3.2 Hypothesis Testing and Confidence Intervals - Video: Statistical Learning: 3.3 Multiple Linear Regression - Video: Statistical Learning: 3.4 Some important questions - Video: Statistical Learning: 3.5 Extensions of the Linear Model - Video: Statistical Learning: 3.Py Linear Regression and statsmodels Package - 2023 - Video: Statistical Learning: 3.Py Multiple Linear Regression Package - 2023 - Video: Statistical Learning: 3.Py Interactions, Qualitative Predictors and Other Details I 2023
Week 3	Classification	Ch. 4	slides book lab	- Video: Statistical Learning: 4.1 Introduction to Classification Problems - Video: Statistical Learning: 4.2 Logistic Regression - Video: Statistical Learning: 4.3 Multivariate Logistic Regression - Video: Statistical Learning: 4.4 Logistic Regression Case Control Sampling and Multiclass - Video: Statistical Learning: 4.5 Discriminant Analysis - Video: Statistical Learning: 4.6 Gaussian Discriminant Analysis (One Variable) - Video: Statistical Learning: 4.7 Gaussian Discriminant Analysis (Many Variables) - Video: Statistical Learning: 4.8 Generalized Linear Models - Video: Statistical Learning: 4.9 Quadratic Discriminant Analysis and Naive Bayes - Video: Statistical Learning: 4.Py Logistic Regression I 2023 - Video: Statistical Learning: 4.Py Linear Discriminant Analysis (LDA) I 2023 - Video: Statistical Learning: 4.Py K-Nearest Neighbors (KNN) I 2023
Week 4	Resampling Methods	Ch. 5	slides book lab	- Video: Statistical Learning: 5.1 Cross Validation - Video: Statistical Learning: 5.2 K-fold Cross Validation - Video: Statistical Learning: 5.3 Cross Validation the wrong and right way - Video: Statistical Learning: 5.4 The Bootstrap - Video: Statistical Learning: 5.5 More on the Bootstrap - Video: Statistical Learning: 5.Py Cross-Validation I 2023 - Video: Statistical Learning: 5.Py Bootstrap I 2023 - Book Chapter: Modern Dive -Bootstrapping and Confidence Intervals
Week 5	Linear Model Selection & Regularization	Ch. 6	slides book lab	- Video: Statistical Learning: 6.1 Introduction and Best Subset Selection - Video: Statistical Learning: 6.2 Stepwise Selection - Video: Statistical Learning: 6.3 Backward stepwise selection - Video: Statistical Learning: 6.4 Estimating test error - Video: Statistical Learning: 6.5 Validation and cross validation - Video: Statistical Learning: 6.6 Shrinkage methods and ridge regression - Video: Statistical Learning: 6.7 The Lasso - Video: Statistical Learning: 6.8 Tuning parameter selection - Video: Statistical Learning: 6.9 Dimension Reduction Methods - Video: Statistical Learning: 6.10 Principal Components Regression and Partial Least Squares - Video: Statistical Learning: 6.Py Stepwise Regression I 2023 - Video: Statistical Learning: 6.Py Ridge Regression and the Lasso I 2023
Week 6	Beyond Linearity	Ch. 7	slides book lab	- Video: Statistical Learning: 7.1 Polynomials and Step Functions - Video: Statistical Learning: 7.2 Piecewise Polynomials and Splines - Video: Statistical Learning: 7.3 Smoothing Splines - Video: Statistical Learning: 7.4 Generalized Additive Models and Local Regression - Video: Statistical Learning: 7.Py Polynomial Regressions and Step Functions I 2023 - Video: Statistical Learning: 7.Py Splines I 2023 - Video: Statistical Learning: 7.Py Generalized Additive Models (GAMs) I 2023
Week 7	Tree-Based Methods	Ch. 8	slides book lab	- Video: Statistical Learning: 8.1 Tree based methods - Video: Statistical Learning: 8.2 More details on Trees - Video: Statistical Learning: 8.3 Classification Trees - Video: Statistical Learning: 8.4 Bagging - Video: Statistical Learning: 8.5 Boosting - Video: Statistical Learning: 8.6 Bayesian Additive Regression Trees - Video: Statistical Learning: 8.Py Tree-Based Methods I 2023
Week 8	Support Vector Machines	Ch. 9	slides book lab	- Video: Statistical Learning: 9.1 Optimal Separating Hyperplane - Video: Statistical Learning: 9.2.Support Vector Classifier - Video: Statistical Learning: 9.3 Feature Expansion and the SVM - Video: Statistical Learning: 9.4 Example and Comparison with Logistic Regression - Video: Statistical Learning: 9.Py Support Vector Machines I 2023 - Video: Statistical Learning: 9.Py ROC Curves I 2023
Week 09	Unsupervised Learning	Ch. 12	slides - TBP book lab	- Video: Statistical Learning: 12.1 Principal Components - Video: Statistical Learning: 12.2 Higher order principal components - Video: Statistical Learning: 12.3 k means Clustering - Video: Statistical Learning: 12.4 Hierarchical Clustering - Video: Statistical Learning: 12.5 Matrix Completion - Video: Statistical Learning: 12.6 Breast Cancer Example - Video: Statistical Learning: 12.Py Principal Components I 2023 - Video: Statistical Learning: 12.Py Clustering I 2023 - Video: Statistical Learning: 12.Py Application: NCI60 Data I 2023
Week 10	Final Project	.	.	.
Week 11	Final Project	.	.	.
Week 12	Final Project	.	.	.
Week 13	Deep Learning	Ch. 10	slides - TBP book lab	- Video: Statistical Learning: 10.1 Introduction to Neural Networks - Video: Statistical Learning: 10.2 Convolutional Neural Networks - Video: Statistical Learning: 10.3 Document Classification - Video: Statistical Learning: 10.4 Recurrent Neural Networks - Video: Statistical Learning: 10.6 Fitting Neural Networks - Video: Statistical Learning: 10.7 Interpolation and Double Descent - Video: Statistical Learning: 10.Py Single Layer Model: Hitters Data I 2023 - Video: Statistical Learning: 10.Py Multilayer Model: MNIST Digit Data I 2023 - Video: Statistical Learning: 10.Py Convolutional Neural Network: CIFAR Image Data I 2023 - Video: Statistical Learning: 10.Py Document Classification and Recurrent Neural Networks I 2023
Week 14	Deep Learning	Ch. 10	.	.
Week 15	Deep Learning	Ch. 10	.	.

* The course slides and labs are based on the ISLP book, “An Introduction to Statistical Learning with Applications in Python” by James, G., Witten, D., Hastie, T., and Tibshirani, R., and have been adapted to suit the specific needs of our course.