Statistical Machine Learning
Course dates: 2018-01-15 -- 2018-03-09.
This is an introductory course to statistical machine learning focusing on classification and regression. The course will cover a range of techniques used in machine learning and data science, including:
- Linear regression, ridge regression and the LASSO
- Classification via logistic regression
- Linear and quadratic discriminant analysis
- Regression and classification trees
- Neural networks and deep learning
These methods will be studied and applied to real data from various applications throughout the course. The course also covers important practical considerations such as cross-validation, model selection and the bias-variance trade-off.
- Lectures: 11 (including an introduction to R)
- Problem solving sessions: 9
- Computer lab: 1
- Mini project: 1
- Exam: Written exam
The course is for 5 credits. Entry requirements are: 120 credits, including Probability and Statistics, Linear Algebra II, Single Variable Calculus, and one basic programming course.