Statistical Machine Learning 1RT700 61808 VT2023
Course content
This is an introductory course to statistical machine learning for students with some background in elementary calculus, linear algebra, and probability theory.
The course is focusing on supervised learning, i.e., classification and regression with real data, and its computational and statistical foundations. In addition to the necessary theory (e.g., derivations and proofs), this course also includes practical sessions (notably the lab and the project). The practical part will be implemented using Python.
The recommended course book is Machine Learning - A First Course for Engineers and Scientists, which is available online free of charge. Certain chapters will be recommended for reading each week. Link to videos and other material are also provided below.
Prerequisite knowledge
The course assumes the students have background in elementary calculus, linear algebra, and probability theory. The following examples are concepts used in the course and it is expected that the students have some familiarity with them:
- Gradient of a function
- Matrix multiplication
- (Joint) probability distributions of random variables
- Expected value of a random variable
- Conditional probability distribution and conditional expected value
We have collected links to additional resources here.
Course structure
- Lectures: 10 + 1
- Exercise sessions: 10+1
- Computer lab: 1
- Project: 1
- Helpdesk: with focus on project
- Exam: 1
Click on the links above to get further information about each course element.
Course evaluation
Mid-course evaluation Final course evaluation Course report
Schedule
The schedule is available in TimeEdit
.
Exercise sessions
During the exercise sessions, the teaching assistant will give a short introduction of the material related to that session and during the session provide insight and comments on the solutions. Except for this, you are supposed to be active and work with the suggested exercises and the teaching assistants will be available for questions and discussion. Take this opportunity to interact! The exercises will be available ahead of time before the session. There are two time slots for each exercise session in the schedule. Choose the one that fits best with your schedule.
Grading
A written final exam is scheduled for March 13th. To pass the course you need to pass the project, lab and exam. Grade VG on the project will increase your grade one step, but not from U to 3.
Teachers
| Course responsible | Jens Sjölund |
| Lecturer | Sebastian Mair |
| Teaching Assistant | Aleix Nieto Juscafresa |
| Teaching Assistant | Aleksander Andrzejewski |
| Teaching Assistant | Duc Huy Le |
| Teaching Assistant | Jennifer Andersson |
| Teaching Assistant | Jingwei Hu |
| Teaching Assistant | Philipp Pilar |
| Teaching Assistant | Ziwei Luo |