Description
The course aims to provide the necessary introductory knowledge for Machine Learning (ML) concepts and demonstrates how to solve real-world problems with ML techniques. It covers the following topics:
Basic concepts of ML
Supervised Learning
Unsupervised Learning
Transfer and Ensemble Learning
Evaluation methods for ML
Machine learning for science
From Machine learning to modern AI
Ethics of ML & AI (explainability and AI alignment)
Course Objectives
Learn key concepts of Machine Learning
Develop practical skills in applying ML(via exercises and coding assignments on Python)
Develop skills in scientific reporting (via assignment report)
Evaluate your new knowledge with the course assignments
Mode of Instruction
Lectures and workshops
Assessment Method
Multiple choices exam 70%
Three individual assignments 30%
A final weighted and rounded average of 6.0 is required to pass the course.
A minimum of 5.5 in each of the three assessments is required.
A non-completed assignment is evaluated as 1.0.
Only the final grade will be rounded (.49 rounded down and .5 rounded up)
Students who are entitled to more exam/retake time must report to info@sbb.leidenuniv.nl 10 days before the exam/retake takes place.
Retakes
The exam's retake is another exam.
There will be no retakes for the assignments.
Registration
Application period
For application EduXchange is used, application will start on Thursday 15th of May 2025 at 13:00h.
For minor students, TU Delft, Erasmus and LDE students: Thursday 15 May 13.00h until 30 June
More information about the application procedure can be found on this website: