Course Applied Machine Learning
Bachelor Information Science
This is the information of Spring 2025
In the previous years the course was given by Maarten Marx.
Contents
he 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
Literature
The course is based on the book Introduction to Machine Learning with Python.
Links
- The 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 November 12, 2024
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