Schedule

Author

Davi Moreira

Week Topic Readings ISLP Material* Supplementary Materials
Week 1 Syllabus, Logistics, and Introduction. Ch. 1; Ch. 2; slides
book lab		 - Video: Statistical Learning: 2.1 Introduction to Regression Models
- Video: Statistical Learning: 2.2 Dimensionality and Structured Models
- Video: Statistical Learning: 2.3 Model Selection and Bias Variance Tradeoff
- Video: Statistical Learning: 2.4 Classification
- Video: Statistical Learning: 2.Py Data Types, Arrays, and Basics - 2023
- Video: Statistical Learning: 2.Py.3 Graphics - 2023
- Video: Statistical Learning: 2.Py Indexing and Dataframes - 2023
Week 2 Linear Regression Ch. 3. slides
book lab		 - Video: Statistical Learning: 3.1 Simple linear regression
- Video: Statistical Learning: 3.2 Hypothesis Testing and Confidence Intervals
- Video: Statistical Learning: 3.3 Multiple Linear Regression
- Video: Statistical Learning: 3.4 Some important questions
- Video: Statistical Learning: 3.5 Extensions of the Linear Model
- Video: Statistical Learning: 3.Py Linear Regression and statsmodels Package - 2023
- Video: Statistical Learning: 3.Py Multiple Linear Regression Package - 2023
- Video: Statistical Learning: 3.Py Interactions, Qualitative Predictors and Other Details I 2023
Week 3 Classification Ch. 4 slides
book lab		 - Video: TBP
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Week 4 Resampling Methods Ch. 5 slides
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Week 5 Linear Model Selection & Regularization Ch. 6 slides
book lab		 - Video: TBP
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Week 6 Beyond Linearity Ch. 7 slides
book lab		 - Video: TBP
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Week 7 Tree-Based Methods Ch. 8 slides
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Week 8 Support Vector Machines Ch. 9 slides
book lab		 - Video: TBP
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Week 09 Unsupervised Learning Ch. 12 slides
book lab		 - Video: TBP
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Week 10 Final Project . . .
Week 11 Final Project . . .
Week 12 Final Project . . .
Week 13 Deep Learning Ch. 10 slides
book lab		 - Video: TBP
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Week 14 Deep Learning Ch. 10 . .
Week 15 Deep Learning Ch. 10 . .

* The course slides and labs are based on the ISLP book, “An Introduction to Statistical Learning with Applications in Python” by James, G., Witten, D., Hastie, T., and Tibshirani, R., and have been adapted to suit the specific needs of our course.