Statistical Machine Learning
Course dates: 2020-01-20 -- 2020-03-16.
Information about the home exam can be found here
This is an introductory course to statistical machine learning for students with some background in calculus, linear algebra and statistics. The course is focusing on supervised learning, i.e, classification and regression. The course will cover a range of methods used in machine learning and data science, including:
- Linear regression (including ridge regression and the Lasso)
- Classification via logistic regression and k nearest neighbor
- Linear and quadratic discriminant analysis
- Regression and classification trees (including bagging and random forests)
- 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. The course includes theory (e.g., derivations and proofs) as well as practice (notably the lab and the mini project). The practical part will be implemented using Python.
- Lectures: 11 (including an introduction to Python and scikit-learn)
- Problem solving sessions: 10
- Computer lab: 1 (mandatory)
- Mini project: 1 (mandatory)
- Exam: Written exam (mandatory)
- Literature: Lecture notes
- Language of instruction: English
The course schedule is available in TimeEdit.
The course is 5 credits. Entry requirements are: 120 credits, including Probability and Statistics, Linear Algebra II, Single Variable Calculus, and one basic programming course.
To pass the course you need to pass the mini-project, lab and exam. If you pass mini-project and lab, your exam grade will also be your course grade. Grade VG on the mini-project will increase your course grade by one step, but not from U to 3.