²ÊÃñ±¦µä

Introduction to overarching and foundational concepts in Statistical Learning like Supervised vs Unsupervised, Prediction, Inference, Interpretability vs Flexibility, Parametric Methods, Quantitative vs. Qualitative, etc.

Exploration into assessing models in different situations. How do we define a "best" model for given data?

Introduction to Simple Linear Regression, such as when and how to use it.

 A deep dive into multiple linear regression, a strong and extremely popular technique for a continuous target.

Exploration of Linear Regression pitfalls and the strengths of Logistic Regression in certain situations. Foundational generative models will also be covered.

Investigation of popular classification techniques, such as LDA and QDA. We will also explore another Regression, Poisson Regression, as well as link functions, which connect all Regressions together.

You will complete a programming assignment worth 31% of your grade. You must attempt the final in order to earn a grade in the course. If you've upgraded to the for-credit version of this course, please make sure you review the additional for-credit materials in the Introductory module and anywhere else they may be found.