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Objectives:
This course aims to give students a solid understanding of machine learning (ML) models and techniques, covering both foundational principles and their practical applications.
After successful completion, students will be able to:
Course description:
This course provides a comprehensive introduction to the fundamental principles of machine learning and statistical pattern recognition. It covers both the theoretical foundations and practical implementation of machine learning methods, guiding participants through the end-to-end process of data investigation using machine learning techniques. The objective is to either uncover new insights in areas with limited prior knowledge or achieve accurate predictions of future observations.
Beginning with an overview and characterization of machine learning methods, the course delves into general principles for data manipulation, feature engineering, model selection, calibration, and evaluation. It then focuses on supervised learning, specifically tree-based regression and classification models, which are currently considered state-of-the-art for tabular data as well as on Gaussian processes.
The morning sessions primarily emphasize theoretical aspects, while the afternoon sessions offer hands-on demonstrations of machine learning methods using Python. The course does not center around specific applications, as those are addressed in the optional project. Participants are encouraged to apply the foundational knowledge gained in the course to a machine learning application relevant to their own scientific domain. Throughout the sessions, examples of machine learning applications are provided for reference
Assignment:
Program:
| Day 1 | Morning Session: Introduction to the foundational ideas of ML. Supervised, unsupervised and reinforcement learning. History of ML and overview of different methods.
Afternoon Session: An introduction to the fundamentals of ML in Python
Instructor: David Wozabal, VU Amsterdam
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| Day 2 | Morning Session: The ML pipeline including visualization of data, feature engineering, training testing and validation of models, hyperparameter tuning and cross validation, trees for regression and classification.
Afternoon Session: Pandas dataframes, plotting in Python, descriptive data analysis, an extended example of data cleaning and feature engineering.
Instructor: David Wozabal, VU Amsterdam
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| Day 3 | Morning Session: Random forests, creating strong learners from weak learners (boosting and bagging), boosted trees as state of the art ML for tabular data
Afternoon Session: An extended regression example using XGBoost in Scikit learn.
Instructor: David Wozabal, VU Amsterdam
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| Day 4 | Morning Session: Gaussian processes regression. Introduction to Bayesian optimization.
Afternoon Session: Example on GPR and BO, and joint exercise.
Instructor: Inneke Van Nieuwenhuyse, Hasselt University |
Literature:
Methodology:
Course material:
The material consists of slides that will be made available to the students before class, and that contain references for further reading. There is no mandatory reading to prepare for the course, but interested students can already refer to the following recommended textbooks (optional):
Prerequiste:
Contact
GP-OML office
Conchita van der Stelt
c.vanderstelt@rstrail.nl
+31(0)15 27 89256