Curriculum
- 13 Sections
- 108 Lessons
- 10 Weeks
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- Statistical Learning with Python4
- Regression Models8
- 2.1Statistical Learning: 2.1 Introduction to Regression Models
- 2.2Statistical Learning: 2.2 Dimensionality and Structured Models
- 2.3Statistical Learning: 2.3 Model Selection and Bias Variance Tradeoff
- 2.4Statistical Learning: 2.4 Classification
- 2.5Statistical Learning: 2.Py Setting Up Python I 2023
- 2.6Statistical Learning: 2.Py Data Types, Arrays, and Basics I 2023
- 2.7Statistical Learning: 2.Py.3 Graphics I 2023
- 2.8Statistical Learning: 2.Py Indexing and Dataframes I 2023
- Linear Regression8
- 3.1Statistical Learning: 3.1 Simple linear regression
- 3.2Statistical Learning: 3.2 Hypothesis Testing and Confidence Intervals
- 3.3Statistical Learning: 3.3 Multiple Linear Regression
- 3.4Statistical Learning: 3.4 Some important questions
- 3.5Statistical Learning: 3.5 Extensions of the Linear Model
- 3.6Statistical Learning: 3.Py Linear Regression and statsmodels Package I 2023
- 3.7Statistical Learning: 3.Py Multiple Linear Regression Package I 2023
- 3.8Statistical Learning: 3.Py Interactions, Qualitative Predictors and Other Details I 2023
- Classification Problems12
- 4.1Statistical Learning: 4.1 Introduction to Classification Problems
- 4.2Statistical Learning: 4.2 Logistic Regression
- 4.3Statistical Learning: 4.3 Multivariate Logistic Regression
- 4.4Statistical Learning: 4.4 Logistic Regression Case Control Sampling and Multiclass
- 4.5Statistical Learning: 4.5 Discriminant Analysis
- 4.6Statistical Learning: 4.6 Gaussian Discriminant Analysis (One Variable)
- 4.7Statistical Learning: 4.7 Gaussian Discriminant Analysis (Many Variables)
- 4.8Statistical Learning: 4.8 Generalized Linear Models
- 4.9Statistical Learning: 4.9 Quadratic Discriminant Analysis and Naive Bayes
- 4.10Statistical Learning: 4.Py Logistic Regression I 2023
- 4.11Statistical Learning: 4.Py Linear Discriminant Analysis (LDA) I 2023
- 4.12Statistical Learning: 4.Py K-Nearest Neighbors (KNN) I 2023
- Cross Validation7
- 5.1Statistical Learning: 5.1 Cross Validation
- 5.2Statistical Learning: 5.2 K-fold Cross Validation
- 5.3Statistical Learning: 5.3 Cross Validation the wrong and right way
- 5.4Statistical Learning: 5.4 The Bootstrap
- 5.5Statistical Learning: 5.5 More on the Bootstrap
- 5.6Statistical Learning: 5.Py Cross-Validation I 2023
- 5.7Statistical Learning: 5.Py Bootstrap I 2023
- Best Subset Selection12
- 6.1Statistical Learning: 6.1 Introduction and Best Subset Selection
- 6.2Statistical Learning: 6.2 Stepwise Selection
- 6.3Statistical Learning: 6.3 Backward stepwise selection
- 6.4Statistical Learning: 6.4 Estimating test error
- 6.5Statistical Learning: 6.5 Validation and cross validation
- 6.6Statistical Learning: 6.6 Shrinkage methods and ridge regression
- 6.7Statistical Learning: 6.7 The Lasso
- 6.8Statistical Learning: 6.8 Tuning parameter selection
- 6.9Statistical Learning: 6.9 Dimension Reduction Methods
- 6.10Statistical Learning: 6.10 Principal Components Regression and Partial Least Squares
- 6.11Statistical Learning: 6.Py Stepwise Regression I 2023
- 6.12Statistical Learning: 6.Py Ridge Regression and the Lasso I 2023
- Polynomials and Step Functions7
- 7.1Statistical Learning: 7.1 Polynomials and Step Functions
- 7.2Statistical Learning: 7.2 Piecewise Polynomials and Splines
- 7.3Statistical Learning: 7.3 Smoothing Splines
- 7.4Statistical Learning: 7.4 Generalized Additive Models and Local Regression
- 7.5Statistical Learning: 7.Py Polynomial Regressions and Step Functions I 2023
- 7.6Statistical Learning: 7.Py Splines I 2023
- 7.7Statistical Learning: 7.Py Generalized Additive Models (GAMs) I 2023
- Tree based methods7
- 9.1Statistical Learning: 8.1 Tree based methods
- 9.2Statistical Learning: 8.2 More details on Trees
- 9.3Statistical Learning: 8.3 Classification Trees
- 9.4Statistical Learning: 8.4 Bagging
- 9.5Statistical Learning: 8.5 Boosting
- 9.6Statistical Learning: 8.6 Bayesian Additive Regression Trees
- 9.7Statistical Learning: 8.Py Tree-Based Methods I 2023
- Optimal Separating Hyperplane6
- 10.1Statistical Learning: 9.1 Optimal Separating Hyperplane
- 10.2Statistical Learning: 9.2.Support Vector Classifier
- 10.3Statistical Learning: 9.3 Feature Expansion and the SVM
- 10.4Statistical Learning: 9.4 Example and Comparison with Logistic Regression
- 10.5Statistical Learning: 9.Py Support Vector Machines I 2023
- 10.6Statistical Learning: 9.Py ROC Curves I 2023
- Neural Networks11
- 11.1Statistical Learning: 10.1 Introduction to Neural Networks
- 11.2Statistical Learning: 10.2 Convolutional Neural Networks
- 11.3Statistical Learning: 10.3 Document Classification
- 11.4Statistical Learning: 10.4 Recurrent Neural Networks
- 11.5Statistical Learning: 10.5 Time Series Forecasting
- 11.6Statistical Learning: 10.6 Fitting Neural Networks
- 11.7Statistical Learning: 10.7 Interpolation and Double Descent
- 11.8Statistical Learning: 10.Py Single Layer Model: Hitters Data I 2023
- 11.9Statistical Learning: 10.Py Multilayer Model: MNIST Digit Data I 2023
- 11.10Statistical Learning: 10.Py Convolutional Neural Network: CIFAR Image Data I 2023
- 11.11Statistical Learning: 10.Py Document Classification and Recurrent Neural Networks I 2023
- Survival Data and Censoring6
- 12.1Statistical Learning: 11.1 Introduction to Survival Data and Censoring
- 12.2Statistical Learning: 11.2 Proportional Hazards Model
- 12.3Statistical Learning: 11.3 Estimation of Cox Model with Examples
- 12.4Statistical Learning: 11.4 Model Evaluation and Further Topics
- 12.5Statistical Learning: 11.Py Cox Model: Brain Cancer Data I 2023
- 12.6Statistical Learning: 11.Py Cox Model: Publication Data I 2023
- Principal Components9
- 13.1Statistical Learning: 12.1 Principal Components
- 13.2Statistical Learning: 12.2 Higher order principal components
- 13.3Statistical Learning: 12.3 k means Clustering
- 13.4Statistical Learning: 12.4 Hierarchical Clustering
- 13.5Statistical Learning: 12.5 Matrix Completion
- 13.6Statistical Learning: 12.6 Breast Cancer Example
- 13.7Statistical Learning: 12.Py Principal Components I 2023
- 13.8Statistical Learning: 12.Py Clustering I 2023
- 13.9Statistical Learning: 12.Py Application: NCI60 Data I 2023
- Hypothesis Testing11
- 14.1Statistical Learning: 13.1 Introduction to Hypothesis Testing
- 14.2Statistical Learning: 13.1 Introduction to Hypothesis Testing II
- 14.3Statistical Learning: 13.2 Introduction to Multiple Testing and Family Wise Error Rate
- 14.4Statistical Learning: 13.3 Bonferroni Method for Controlling FWER
- 14.5Statistical Learning: 13.4 Holm’s Method for Controlling FWER
- 14.6Statistical Learning: 13.5 False Discovery Rate and Benjamini Hochberg Method
- 14.7Statistical Learning: 13.6 Resampling Approaches
- 14.8Statistical Learning: 13.6 Resampling Approaches II
- 14.9Statistical Learning: 13.Py Multiple Testing I 2023
- 14.10Statistical Learning: 13.Py False Discovery Rate I 2023
- 14.11Statistical Learning: 13.Py Multiple Testing and Resampling I 2023
Statistical Learning: 8 Years Later (Second Edition of the Course)
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