| Univ. Belgrade Prof Dr. A. Kovačević | Astroinformatics – Astrostatistics and Machine Learning in Astronomy (S3, elective, 6 ECTS) |
| Learning Outcomes: | This course will address following learning priorities: Communication, Critical Thinking, Information Literacy, Self-Directed Learning, and Technology Use. Upon completion of this course, students will be able to handle and apply tools and techniques for processing large data in their original research areas as well as for eventual applications in the space industry. |
| Knowledge and Understanding: | Astroinformatics is an interdisciplinary field of study involving the combination of astronomy , data science , informatics , and information / communications technologies.The primary focus of this course is on the world wide distributed collection of digital astronomical databases, image archives, and research tools used for analyzing these astronomical observations. |
| Applying Knowledge and Understanding: | All lectures and tutorials are designed to equip student to with a wide range of IT tools that are essential for the modern astronomer. These include programming, Unix scripting, database construction and use, internet technologies and data mining. Most sessions are interactive. The advantage of this is that students can continue to use all the software (e.g. scripting, databases etc) that they have been working on. |
| Prerequisites | Experience with Anaconda Python, SciPy, NumPy, as well as GitHub would be preferred, but is not required. |
| Program | -Large collection of images analysis: i.e. application of kernel convolution for detection of satellite galaxies of the Milky Way – Databases: Dark Energy Survey, LSST, EELT – Specific tools for accessing and mining data: Data Lab, World Coordinate System (astropy.wcs) – Specific tools for statistical operation on large astronomical databases: astroML – High Performance Computing: training on Hewitt Packard supercomputer |
| Description of how the course is conducted | Classes will be roughly evenly divided between lecture and hands-on practice. |
| Description of the didactic methods | Students will be working through code examples during the course of the lecture. Contributions of students real-time solutions will contribute to enhancement of their class participation score. There will be a programming/reading assignment for homework in order to to help synthesize the topics covered in lectures. Three or one person groups will research/conduct a project. Students will write a 6+ page paper (not including figures [which are strongly suggested] and references [required]) and give a 20-minute presentation to the class. Presentations are strongly recommended to be in the form of a Jupyter notebook. Groups should clearly delineate the roles of each student in the project. |
| Description of the evaluation methods | The final exam will be orally and practically defended term project which is defined at the beginning of the semester. |
| Adopted Textbooks | -Jake VanderPlas, Python Data Science Handbook, 2017, O’Reilly Media, –Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Databy Ivezic, Connolly, VanderPlas, and Gray (ISBN: 9780691151687). -Relevant Astronomical Databases Manuals -Relevant online astroinformatics tools manuals |
| Recommended readings | Students will also be required to gain practical experience with broad types of data sets using the software learning environment at datacamp.com, which can also serve as a refresher for Python programming. |
