STAT 3450

Statistical learning with R

Lec 01 selected

This course teachesstudents how to use the R software environment to analyze data of various types. Students will be given the opportunity to write their own programs to explore data and solve meaningful problems. Emphasis will be given to a good understanding of supervised/unsupervised learning models, their founding principles, and to data processing and interpretation.

Sections

Lecture

From the syllabus

From the Winter 2025 syllabus by Théo Michelot. Open the PDF

Grading

20%Assignments (5)4% each; every two weeks
5%Project proposalFebruary 9
10%Project outlineMarch 16
20%Project reportApril 6
15%Midterm examFebruary 25
30%Final examDate set by registrar

Textbook

  • An Introduction to Statistical Learning, with Applications in R (Second Edition), James, Witten, Hastie & Tibshirani (free)

Course rules

Late work
Assignments lose 10% per day, up to three days; later submissions receive zero.
Assignment collaboration
High-level discussion is allowed, but solutions must be written independently and not shared.
Generative AI
AI may help with ideas and code; disclose its use, do not copy generated text, and verify accuracy.
Lectures
Lectures are in person and will not be recorded.

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