Course syllabus

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:

Week 1: Introduction, Recap of Probability Theory, Linear Regression.

Week 2-3: Logistic regression, k-NN and discriminant analysis.

Week 3-4: Bias-variance trade-off, cross validation.

Week 4-5: Tree-based methods, bagging and boosting.

Week 5-6: Feature selection, applications and ethics

Week 6-8: Neural networks.

Week 9: Exam.

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.

For those who haven't used Python before there is an introductory video here.

Reading and video material

The  book for this course is The Supervised Machine Learning book. It is available online for free. Certain chapters will be recommended reading each week. 

We also link to videos and other material. There is a lot of very good video material on Youtube, which can help in your learning. Use it as much as you like!

What you need to do each week

Each week will consist of a mixture of lectures, video material, reading, paper and pen and computer classes.  You will get the most from the course if you attend the lectures live (although all videos will be made available afterward too) and attempt the pen-and-paper and computer classes before the tutorial sessions.

Answers to tutorial will be posted directly before the tutorial sessions.

The course schedule is available in TimeEdit. 

You are expected to work 10-15 hours a week on this course. A suggested time to invest in the course is suggested at the start of each week. If you do this work you will, with a high probability, pass the exam.

Mini-project

Go here for details of the mini-project. 

Results of the best performance on the mini-project are published here.

Neural network computer lab