701-3003-00L Environmental Systems Data Science: Machine Learning
| Semester | Autumn Semester 2022 |
| Lecturers | L. Pellissier, E. J. Harris, J. Payne, M. Volpi |
| Periodicity | yearly recurring course |
| Language of instruction | English |
| Comment | Number of participants is limited to 80. Course registration starts on 31.08.2022. Priority is given to the target groups until 23.09.2022, Target groups Agricultural Sciences MSc Environmental Sciences MSc Atmospheric and Climate Science MSc Environmental Sciences PhD Agricultural Sciences PhD Waiting list will be deleted on 30.09.2022 |
Courses
| Number | Title | Hours | Lecturers | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 701-3003-00 G | Environmental Systems Data Science: Machine Learning | 2 hrs |
| L. Pellissier, E. J. Harris, J. Payne, M. Volpi |
Catalogue data
| Abstract | Students are introduced to advanced data science where environmental data are analyzed using state of the art machine learning methods. Starting from known statistical approaches, they learn the principle of more advanced machine learning methods with practical application. The course enables students to plan their own data science project in their specialization and to apply machine learning mode |
| Learning objective | The students are able to • select an appropriate model related to a research question and dataset • describe the steps from data preparation to running and evaluating models • prepare data for running machine learning with dependent and independent variable • build and validate regressions and neural network models • understand convolution and deep learning models • access online resources to keep up with the latest data science methodology and deepen their understanding |
| Content | • The data science workflow • Data preparation for running and validating machine learning models • Get to know machine learning approaches including regression, random forest and neural network • Model complexity and hyperparameters • Model parameterization and loss • Model evaluations and uncertainty • Deep learning with convolutions |
| Literature | Building on existing data science resources |
| Prerequisites / Notice | Math IV, VI (Statistics); R, Python; ESDS I |
Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 3 credits |
| Examiners | L. Pellissier, E. J. Harris, J. Payne, M. Volpi |
| Type | ungraded semester performance |
| Language of examination | English |
| Repetition | Repetition only possible after re-enrolling for the course unit. |
Learning materials
| No public learning materials available. | |
| Only public learning materials are listed. |
Groups
| No information on groups available. |
Restrictions
| Places | 80 at the most |
| Beginning of registration period | Registration possible from 31.08.2022 |
| Priority | Registration for the course unit is until 23.09.2022 only possible for the primary target group |
| Primary target group | Atmospheric and Climate Science MSc (661000)
Environmental Sciences MSc (736000) Doctorate Environmental Sciences (739002) Doctorate Agricultural Sciences (739102) Agricultural Sciences MSc (762000) |
| Waiting list | until 30.09.2022 |
