Course Applied Machine Learning
Bachelor Information Science
This is the information of Spring 2026
Earlier version of this course were given by Maarten Marx and Evangelos Kanoulas.
Contents
The underlying question behind this course is how to algorithmically extract valuable information from raw data. Data Mining is an analytic process designed to explore data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. The technological topics that will be covered in this course are:- Classification algorithms
- Logistic regression
- Perceptron and neural networks
- Decision trees
- Support vector machines
- Frequent pattern matching
- Clustering algorithms
In the practical assignments you will you'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library.
Learning objectives
See the course description for the full list of learning objectives. Yet, as Information Science you will propably will find a job as Data Scientist, which means that- as graduate you know how to translate an information request into an innovative Data Science application
Schedule
The detailed schedule can be found at datanose.nl Here is already a preliminary schedule for 2027
- Week 13: Overview of the course
- Week 13: Introduction to Machine Learning
- Week 13: Linear Regression
- Week 13: K-Neighbors Regression
- Week 14: Polynomial Regression
- Week 14: Non-Linear Regression
- Week 15: Controlling Complexity
- Week 15: Multiple Features
- Week 15: Decision-Tree Regression
- Week 15: Support Vector Machines for Regression
- Week 16: Practice Mid-Term Exam
- Week 17: Classification methods
- Week 17: Classification evaluation
- Week 17: Tree-based Classifiers
- Week 17: Cross Validation
- Week 18: Spring break
- Week 19: Support Vector Machines for Classification
- Week 19: Grid Search
- Week 19: Clustering
- Week 19: Pipeling
- Week 20: Dimensionality Reduction
- Week 20: Machine Learning Highlights
Literature
The course is based on the book Introduction to Machine Learning with Python. This is a course with code.
Links
- Supporting material is from Python Data Science Handbook, which is used in the 3rd year.
- The Data Science assignments are based on this Jupyter notebooks
- The author, Jake VanderPlas, is an software engineer at Google Research who contributed to Skikit-Learn and Scipy.
- Jake's blog: Pythonic Perambulations (last post Sep. 2018)
Last updated February 13, 2026
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