F5611 Introduction to Machine learning for astronomers in Python

Faculty of Science
Autumn 2024
Extent and Intensity
1/1/0. 3 credit(s). Type of Completion: z (credit).
Teacher(s)
Mgr. Matej Kosiba, Ph.D. (seminar tutor)
Mgr. Tomáš Plšek (seminar tutor)
Dr. Martin Topinka, PhD. (seminar tutor)
Guaranteed by
Dr. Martin Topinka, PhD.
Department of Theoretical Physics and Astrophysics – Physics Section – Faculty of Science
Contact Person: Dr. Martin Topinka, PhD.
Supplier department: Department of Theoretical Physics and Astrophysics – Physics Section – Faculty of Science
Timetable
Thu 16:00–16:50 Kontaktujte učitele, Thu 17:00–17:50 Kontaktujte učitele
Prerequisites (in Czech)
Basics in programming in Python
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives (in Czech)
The course serves as a non-mathematical introduction to the concept of Machine Learning. The students will get familiar with the state-of-art Machine Learning libraries in Python programming language and will be able to apply the learnt methods in various situations and tasks, with impact on the usage in astronomy.
Learning outcomes (in Czech)
Upon successfully completing the course, the student will be able to:
- understand the concept of Machine Learning
- understand and use several popular Machine Learning algorithms within the framework of the scikit-learn library for the Python programming language
understand the concept of deep learning and apply it in the framework of Keras /Tensorflow library
- define and complete a Machine Learning project of his/her own
Syllabus (in Czech)
  • Introduction to Machine Learning, the idea of supervised, non-supervised learning, semi-supervised learning, Classification vs Regression
  • General concept of Machine Learning, loss function
  • Popular algorithms such as Support Vector Machine, Bayesian Regression, K-Nearest neighbours
  • Data (feature) reduction such as Principal Component Analysis
  • Practical use of scikit-learn Python library
  • Model validation techniques. Fine-tuning model parameters.
  • Principles of Deep Learning
  • Practical use of Keras Deep Learning Python library
  • Student's own project
Teaching methods (in Czech)
50% of time lecturing, 50% practical exercise
Language of instruction
English
Further Comments
Study Materials
The course can also be completed outside the examination period.
The course is taught annually.
The course is also listed under the following terms Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2024/F5611