Introduction to Machine Learning

TitleTimeRoomTeacher
Introduction to Machine Learning09.10.2025 14:00 - 17:00 (Thu)nnDr Claudia Serrano Colome
Introduction to Machine Learning16.10.2025 14:00 - 17:00 (Thu)nnDr Claudia Serrano Colome
Introduction to Machine Learning23.10.2025 14:00 - 17:00 (Thu)nnDr Claudia Serrano Colome
Introduction to Machine Learning30.10.2025 14:00 - 17:00 (Thu)nnDr Claudia Serrano Colome
Keywords: 
machine learning, supervised learning, classification, regression, model evaluation, data preprocessing, algorithms
Course Description: 

This course provides an accessible, hands-on introduction to Machine Learning tailored to PhD students in scientific fields. Participants will gain a solid understanding of foundational concepts, algorithms, and workflows in Machine Learning. Emphasis is placed on applying these methods in real research contexts using Python.

Course aims: 
  • Understand what is ML and learn how to preprocess and analyze datasets for ML applications
  • Understand key concepts in supervised learning, such as classification and regression
  • Understand key concepts in unsupervised learning, such as clustering
  • Gain practical experience with tools like scikit-learn and Jupyter notebooks
  • Evaluate and interpret model performance using relevant metrics
Teaching methods: 

Interactive lectures, conceptual slides, coding exercises, hands-on notebooks, real-world datasets

This course fits doctoral candidates in the following phase: 

☒ Beginn der Promotion / Beginning of the doctorate
☒ Während der Promotion / During the doctorate
☒ Endphase der Promotion / End of the doctorate

Participation requirements: 

Basic knowledge of Python programming is expected; no prior experience with machine learning is required.

Technical requirements: 

Laptop with Python 3 installed and Jupyter Notebook (I can send instructions prior to the course)

Course preparation: 

Participants will receive a short guide including software installation instructions and a Python refresher notebook

Additional information: 

Course materials and code notebooks will be shared via a GitHub repository. Participants are encouraged to bring a dataset from their own field if they wish to discuss practical applications.

Category: 
Fachspezifische Veranstaltung
Event type: 
Seminar/Workshop
Organizer: 
Graduate Center of Life Sciences
Responsibility for event: 
Hauptverantwortung
Format: 
In Präsenz
Course Language: 
EN
Course Capacity (Max): 
20
Duration in hours: 
12
Trainer: 
Dr. Claudia Serrano Colome

Claudia Serrano Colome has a background in Mathematics and Physics. She completed her MSc at the University of Oxford in Mathematical Modelling and Scientific Computing and obtained a PhD in Bioinformatics from the CRG in Barcelona, in 2024. She is currently working as a Machine Learning Engineer in Munich. She is passionate about making complex concepts accessible and enjoys teaching practical and foundational skills in AI and data science.