Concepts
•	Fundamentals of machine learning, including cross-validation, loss functions and model fit, and feature selection
•	Tradeoffs in machine learning design (bias-variance tradeoff, overfitting vs. underfitting, parameter tuning)
•	Supervised learning for both regression and classification problems
•	Unsupervised learning and dimension reduction
•	Use of machine learning models for prescriptive applications in business
•	Deployment and maintenance of machine learning models
Techniques
•	Supervised learning in R (linear regression, logistic regression, decision trees, SVM, neural networks)
•	Unsupervised learning in R (centroid clustering, mixture models)
•	Conducting cross-validation as part of a machine learning training pipeline
•	Conducting simulations to test alternative policy choices
•	Implementing a cloud-based machine learning pipeline

Module 1: Basics of Machine Learning
•	Introduction to loss functions, features, labels, and parameters
•	Basics of linear algebra, calculus, and probability
•	Training versus testing data and cross-validation
•	Bias-variance tradeoff, overfitting, and underfitting
Module 2: Regression Models
•	Linear regression in R
•	Interaction terms and dummy variables
•	Generalized linear models
•	Regularization 
Module 3: Classification Models
•	Logistic regression
•	Tree-based models
•	Support vector machines
•	Neural networks
Module 4: Unsupervised Methods
•	Centroid methods such as K-means
•	Mixture models
•	Dimension reduction, including PCA and factor analysis
Module 5: Prescriptive Models
•	Basic optimization concepts, with an introduction to linear programming
•	Simulation in R, including Monte Carlo methods 
Module 6: Deployment
•	Basic design principles of a machine learning pipeline
•	Integrating machine learning models into a cloud environment
•	Best practices for maintaining and updating the pipeline

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