DS 3030 Concepts and Applications of Machine Learning
Textbook
The required textbook for the course is Introduction to Statistical Learning.
An optional, more advanced text is Elements of Statistical Learning.
Software
This course will utilize the statistical software R using either the RStudio or Positron IDE.
Relevant course pages
Course Description
Machine learning concepts such as training and test sets; feature extraction; principles of machine learning techniques; regression; pattern recognition methods; unsupervised learning techniques; assessment and diagnostics: overfitting, error rates, residual analysis, model assumptions checking, feature selection; ethical issues in data science; communicating findings to stakeholders in written, oral, visual and electronic form.
Course Objectives
Students passing this course will be able to
Prerequisite
Prereq: DS 2010, DS 2020, MATH 2070, MATH 2650, and STAT 3201
Q&A
Please use the Canvas discussion forum.
Reading schedule
See slides page.