Introduction to Machine Learning in Natural Science: Modeling and Applications
seminars, workshops |
In this workshop, lead by Reza Belaghi, we will explore the application of state-of-the-art machine learning models in the field of natural science, using real-world examples and
various data sets. Our goal is to equip participants with the necessary knowledge
and skills to apply machine learning in their research and scientific papers, and
applications (whenever is needed).
The workshop is designed for Master’s and Ph.D. students, researchers, and faculty members from all disciplines within SLU who are interested in applying machine learning models to their field of study.
Upon completion of this workshop, participants will have a basic to intermediate understanding of :
- Why, when, and how to apply some supervised machine learning approachesin real data sets.
- How to train and test some of the machine learning models for optimal performance.
- How to interpret the outcomes of specific machine-learning algorithms for scientific papers and applications.
Time: 2023-11-29 09:00 - 2023-11-30 12:00
City: Zoom
Organiser: Statistics@slu
Additional info:
Workshop leader: Reza Belaghi
Requirements: Some basic knowledge in R programming language and statistics (basic concepts of regression).
Registration is required to participate in the workshop: Link to registation form
Time and date: 9 am-12 noon, on Wednesday and Thursday, 29-30 November 2023
Location:The workshop will be held on Zoom and the link will be sent to the email of the participant
More information: Machine learning workshop 29-30 Nov.pdf
The workshop will mainly consist of lectures and discussions, complemented by hands-on practice with R codes.
Day 1 (November 29, 9:15 am –12 noon)
- Introduction to machine learning
- Logistic Regression, Penalized Logistic Regression, Boruta variable selection
- Decision Trees, Random Forest (with variable importance)
- Refreshment in R and Examples of Machine Learning in R programming Language
Day 2 (November 30, 9:15am –12 noon)
- Support Vector Machine (linear and non-linear)
- Artificial Neural Networks (single and multiple layers)
- Examples in R programming language
- Discussion and questions
