Conventional Machine Learning

Workshop on Machine Learning organised at the Data Science Center on 20 May 2022.

Conventional Machine Learning

In the past decade, Deep Learning, originally a subfield of Machine Learning, has gained considerable influence and momentum. In order to distinguish "traditional" Machine Learning such as Random Forest or Regularised Regression from Deep Learning models, we decided to coin this workshop "conventional Machine Learning".

Note: Machine Learning is often abbreviated in ML.

Learning objectives

1. Get familiarised with Machine Learning terminology and terms.
2. Learn about automated Machine Learning: choosing the best ML model

Datasets used

Breast Cancer Wisconsin Data Set (Classification problem)

Taken from the UCI Machine Learning Repository Link

Student grades (Regression problem)

Taken from the UCI Machine Learning Repository Link

References

Credits

Authors:

• Iris van der Knaap @Library, UvA Data Science Center Digital Skills Coordinator.
• Casper Thuis, data scientist @IBED, UvA.
• Marc Galland, support data scientist, @SILS, UvA.

Sources of inspiration

1. Machine Learning at the Vrije University of Amsterdam: https://mlvu.github.io/
2. Genetic programming in Python: https://towardsdatascience.com/genetic-algorithm-implementation-in-python-5ab67bb124a6

TPOT Python package

1. TPOT home page
2. Data Camp tutorial on TPOT

MJLAR

1. MJLAR GitHub repository