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DAT610 - Optimization and Risk Assessment

(SNHU-DAT610.AU1) / ISBN : 978-1-64459-782-8
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1

Introduction

  • An Overview of Statistical Learning
  • A Brief History of Statistical Learning
  • This Course
  • Who Should Read This Course?
  • Notation and Simple Matrix Algebra
  • Organization of This Course
  • Data Sets Used in Labs and Exercises
2

Statistical Learning

  • What is Statistical Learning?
  • Assessing Model Accuracy
  • Lab: Introduction to Python
  • Exercises
3

Linear Regression

  • Simple Linear Regression
  • Multiple Linear Regression
  • Other Considerations in the Regression Model
  • The Marketing Plan
  • Comparison of Linear Regression with K-Nearest Neighbors
  • Lab: Linear Regression
  • Exercises
4

Classification

  • An Overview of Classification
  • Why Not Linear Regression?
  • Logistic Regression
  • Generative Models for Classification
  • A Comparison of Classification Methods
  • Generalized Linear Models
  • Lab: Logistic Regression, LDA, QDA, and KNN
  • Exercises
5

Resampling Methods

  • Cross-Validation
  • The Bootstrap
  • Lab: Cross-Validation and the Bootstrap
  • Exercises
6

Linear Model Selection and Regularization

  • Subnet Selection
  • Shrinkage Methods
  • Dimension Reduction Methods
  • Considerations in High Dimensions
  • Lab: Linear Models and Regularization Methods
  • Exercises
7

Tree-Based Methods

  • The Basics of Decision Trees
  • Bagging, Random Forests, Boosting, and Bayesian Additive Regression Trees
  • Lab: Tree-Based Methods

1

Statistical Learning

  • Implementing the Bayes Classifier
  • Implementing the Bias-Variance Trade-Off
  • Indexing the Data
2

Linear Regression

  • Implementing Simple Linear Regression
  • Performing Multiple Linear Regression
  • Implementing Non-Linear Transformations of Predictors
  • Implementing Qualitative Predictors Using the Credit Dataset
3

Classification

  • Implementing Multiple Logistic Regression
  • Implementing Multinomial Logistic Regression
  • Generating and Visualizing a Multivariate Gaussian Distribution
  • Implementing QDA
  • Implementing Naive Bayes Classification
  • Implementing LDA
4

Resampling Methods

  • Implementing K-Fold Cross-Validation
  • Implementing the Validation Set Approach
  • Implementing LOOCV
  • Implementing KNN on the Caravan Dataset
  • Implementing Poisson Regression
  • Implementing GLM
  • Implementing Bootstrapping Techniques on the Portfolio Dataset
5

Linear Model Selection and Regularization

  • Implementing Subset Selection Methods Using the Hitters Dataset
  • Implementing Forward and Backward Stepwise Selection
  • Implementing Lasso Regression
  • Implementing Ridge Regression
  • Improving Predictions with Principal Components Regression
  • Implementing Partial Least Squares
6

Tree-Based Methods

  • Implementing Bagging and Random Forests
  • Fitting Regression Trees
  • Improving Model Performance Using Boosting
  • Building and Analyzing a Classification Tree Using the Carseats Dataset

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