This course introduced fundamental machine learning models used in analytics, cross-cutting concepts, combining models, and software. There were two major objectives, given a business (or other) question, selecting an appropriate analytics model to answer it, specify the data we will need, and understand what the model's solution will and will not provide as an answer and given someone else's use of analytics to address a specific business (or other) question, evaluate whether they have used an appropriate model (and appropriate data) and whether their conclusion is reasonable. This course included ten assignments, two midterms, a course project, and a final exam. The software used in this course includes R, ARENA/SimPy, and PuLP.

Modeling topics covered in the assignemnts and the project included the following:
• Classification
• Regression
• Clustering
• Change detection
• Time series models
• Basic regression
• Advanced regression
• Tree-based models
• Variable selection
• Design of experiments
• Probability-based models
• Simulation
• Optimization
• Advanced models
• Validation
• Outlier detection
• Scaling/standardization
• Principal component analysis
• Missing data/imputation
• Experiential topics