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79792-01 - Lecture with practical courses: Introduction to Machine Learning with Python for Biology and Environmental Sciences (1 CP)
| Semester | fall semester 2026 |
| Course frequency | Every fall sem. |
| Lecturers | Wandrille Duchemin (wandrille.duchemin@unibas.ch, Assessor) |
| Content | With the rise of new technologies, the volume of data in biology and environmental sciences has grown exponentially recently. A significant issue is to mine useful predictive knowledge from these data. Machine learning (ML) is a discipline in which computer algorithms perform automated learning by using data to assist humans in dealing with large volumes of multidimensional data. The analysis of such data is not trivial, and ML is a necessary tool to extract knowledge and make predictions. This 5-half-day course will introduce participants to common ML algorithms and how to apply them to their data in extensive practical sessions. The practical sessions will be conducted in Python3 based on the widely applied scikit-learn ML framework. The course will comprise a number of hands-on exercises and challenges where the participants will acquire a first understanding of the standard ML methods and processes, as well as the practical skills in applying them to real-world problems using publicly available biological or environmental sciences data sets. |
| Learning objectives | At the end of the course, the participants are expected to: • Explain the ML taxonomy and the commonly used machine learning algorithms for analysing omics data • Describe differences between ML approaches and in which situations they can be applied • Critically evaluate applications of ML in omics studies • Implement common ML algorithms using the scikit-learn Python framework • Interpret and visualize the results obtained from ML analyses |
| Comments | Please register for the course at least two weeks before the start date. |
| Weblink | Quiz to assess your Python skills |
| Admission requirements | Primarily for PhD students at the Department of Environmental Sciences (Master’s students may join with permission). No prior knowledge of ML concepts and methods is required. Familiarity with the Python programming language and pandas data frames, as well as a basic knowledge of statistics is required. Before applying to this course, please assess your Python and statistics skills using the quiz. Please bring your own laptop. Instructions regarding software to install on your computer will be communicated before the course. |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| Block | See individual dates | ||
Dates
| Date | Time | Room |
|---|---|---|
| Monday 12.10.2026 | 09.00-12.30 | Botanik, Seminarraum 00.005 |
| Tuesday 13.10.2026 | 09.00-12.30 | Botanik, Hörsaal 00.003 |
| Wednesday 14.10.2026 | 09.00-12.30 | Botanik, Seminarraum 00.005 |
| Thursday 15.10.2026 | 09.00-12.30 | Botanik, Hörsaal 00.003 |
| Friday 16.10.2026 | 09.00-12.30 | Botanik, Seminarraum 00.005 |
| Modules |
Doctorate Botany: Recommendations (PhD subject: Botany) Doctorate Environmental Sciences: Recommendations (PhD subject: Environmental Sciences) Doctorate Geography: Recommendations (PhD subject: Geography) Doctorate Geosciences: Recommendations (PhD subject: Geosciences) Doctorate Prehistory and Archaeological Science: Recommendations (PhD subject: Prehistory and Archaeological Science) Doctorate Zoology: Recommendations (PhD subject: Zoology) Geography: Recommendations (PhD subject: Geography) |
| Assessment format | continuous assessment |
| Assessment details | Attendance and active participation during the five course days. |
| Assessment registration/deregistration | Reg.: course registration, dereg: cancel course registration |
| Repeat examination | no repeat examination |
| Scale | Pass / Fail |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Science, studiendekanat-philnat@unibas.ch |
| Offered by | Departement Umweltwissenschaften |