DTSA 5020 Regression and Classification
- Specialization: Intro to Statistical Learning
- Instructor: James Bird, Instructor
- Prior knowledge needed: Intro Statistics and Foundational Math
Learning Outcomes
- Express why Statistical Learning is important and how it can be used.
- Identify the strengths, weaknesses and caveats of different models and choose the most appropriate model for a given statistical problem.
- Determine what type of data and problems require supervised vs. unsupervised techniques.
Course Content
Duration: 1h
Introduction to overarching and foundational concepts in Statistical Learning like Supervised vs Unsupervised, Prediction, Inference, Interpretability vs Flexibility, Parametric Methods, Quantitative vs. Qualitative, etc.
Duration: 7h
Exploration into assessing models in different situations. How do we define a "best" model for given data?
Duration: 1h
Introduction to Simple Linear Regression, such as when and how to use it.
Duration: 10h
A deep dive into multiple linear regression, a strong and extremely popular technique for a continuous target.
Duration: 52min
Exploration of Linear Regression pitfalls and the strengths of Logistic Regression in certain situations. Foundational generative models will also be covered.
Duration: 16h
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.
Duration: 15h
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.
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