This course is part of Minor of Data Science and Artificial Intelligence program and it is offered for first time in the academic year 2025-2026. The idea of this page is that we will explain and add the assignments of this course, but also it will contain some additional material that could help you with the mathematics for ML. Note that these materials could be a bit on the challenging side, so revise them with some caution. If you find any part challenging to digest, let us know!
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 I (binary classification and evaluation metrics)
- Supervised learning II (multi-class classification and regression)
- Unsupervised Learning (clustering, descriptive clustering and dimensionality reduction)
- Neural networks
- Decision Trees, Ensemble methods and transfer learning
- Trustworthy AI
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
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 and the exams.
- 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.
Here you can find the assignments of the course.