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Applications of Numerical Analysis: Machine Learning for Atmospheric and Oceanic Bioaerosols

General data

Course ID:	1100-MLAOB
Erasmus code / ISCED:	(unknown) / (unknown)
Course title:	Applications of Numerical Analysis: Machine Learning for Atmospheric and Oceanic Bioaerosols
Name in Polish:	Applications of Numerical Analysis: Machine Learning for Atmospheric and Oceanic Bioaerosols
Organizational unit:	Faculty of Physics
Course groups:	(in Polish) Physics (Studies in English); 2nd cycle
ECTS credit allocation (and other scores):	3.00 Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load.
Language:	English
Main fields of studies for MISMaP:	biology environmental protection geography mathematics physics
Prerequisites (description):	General knowledge of atmospheric physics. Basic knowledge of programming.
Short description:	This course will provide students with a foundation in the research methodologies and techniques used in the field of atmospheric physics, specifically in bioareosol studies in atmosphere and ocean. It will help students gain understanding of numerical analysis and its use in machine learning algorithms. After attending the course students will be able to apply that knowledge in practice numerical solutions to the problem, write simple computer codes for analysis of physical processes in the atmosphere. This lecture is conducted in a hybrid manner and is given only in English.
Full description:	Topics to be covered are listed below, each will be applied to specific problems related for bioaersol detection and typing: 1) Linear algebra concepts 2) Optimization techniques 3) Numerical methods for probability estimation 4) Monte Carlo methods 5) Machine learning algorithm implementations These topics will be applied to specific problems in atmospheric physics.
Bibliography:	The list of references will be provided during the lectures.
Learning outcomes:	This course will provide students with following learning outcomes, according to thedocumentation provided in following documentation: Efekty kształcenia zg. z charakterystyką II stopnia Polskiej Ramy Kwalifikacji dla kwalifikacji uzyskiwanych w ramach szkolnictwa wyższego – poziom 6-8 (http://www.kwalifikacje.gov.pl/images/Publikacje/Polska-rama-kwalifikacji.pdf) 1. Education / Wiedza: P8S-WG 2. Skills / Umiejętności: P6S-UU, P6S-UO, P7S-UW, P8S-UK 3 . Social competences / Kompetencje społeczne: P7S-KR, P8S-KK
Assessment methods and assessment criteria:	The completion of this course will be assessed based on: - 60% home exercises, - 40% written exam result. The topics for the exam will be given 3 weeks in advance and will cover the material realized during lectures. During the exam session one written exam and one make-up exam are planned. Attendance at lectures and tutorials is compulsory.

Classes in period "Winter semester 2023/24" (past)

Time span:	2023-10-01 - 2024-01-28	Selected timetable range: weekly course term Navigate to timetable MO TU W TH WYK FR
Type of class:	Lecture, 30 hours more information
Coordinators:	Iwona Stachlewska
Group instructors:	Artur Tomczak
Students list:	(inaccessible to you)
Examination:	Examination

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