In this course, participants will gain a foundational understanding of dimensionality reduction through a mix of theory and interactive exercises guided by the instructors. The course focuses on three key methods—feature transformation, aggregation, and selection—applied to real-world data using Jupyter notebooks.
This 4-hour course provides an introduction to the topic of dimensionality reduction and serves as a starting point for self-guided learning during and beyond the course time.
The course covers alternating sequences of theoretical input and hands-on exercises, which are discussed with the instructors during the course.
Dimensionality reduction is a common data preprocessing step preceding the application of supervised and unsupervised learning methods in AI modeling. After motivating the use of dimensionality reduction and highlighting its role in data exploration, this course gives an introduction to three types of dimensionality reduction approaches: feature transformation, feature aggregation, and feature selection. Course participants will have the opportunity to discover and compare the main methods for each approach in a hands-on experience, using jupyter notebooks on a real-world high-dimensional dataset.
Learning goals
Day 1: General introduction and feature transformation methods
- General introduction to dimensionality reduction
- Theory and practical application of classical feature transformation methods
- Theory and practical application of autoencoders for feature transformation
Day 2: Further unsupervised and supervised methods
- Theory and practical application of feature aggregation approaches
- Theory and practical application of feature selection methods
- Stability optimization in feature selection
Course date
Register now: May 5, 2015
For more information on how to register, please follow the link on the course date.
Prerequisites
Basic knowledge of Python (as taught in the course “First steps in Python” and “Data processing with Pandas & Data visualization with Matplotlib”). Basic understanding of ML models (as taught in the courses “Machine Learning 1” or “Introduction to Machine Learning”). Google account is recommended.
Target group
This course is open to researchers of all career stages, or anyone interested in learning about the subject.
This course is free of charge.
