Statistical Machine Learning
Course dates: 2017-01-16 -- 2017-03-17.
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:
- Classical and Bayesian linear regression
- Classification via logistic regression
- Linear discriminant analysis
- Gaussian processes and kernel methods
- Regularization (ridge regression and the LASSO)
- 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.
- Lectures: 11 (including an introduction to R)
- Problem solving sessions: 9
- Laboration: 1 (compulsory, part of the examination)
- Mini projeect: 1 (compulsory, part of the examination)
- Exam: Written exam
The course schedule is available in TimeEdit.
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.