Introduction to Machine Learning

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

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.

Lernziele: 
  • 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
Lehrmethoden: 

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

Der Kurs ist für Promovierende in folgender Phase geeignet: 

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

Teilnahmevoraussetzungen: 

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

Technische Voraussetzungen: 

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

Kursvorbereitung: 

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

Sonstige Informationen: 

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.

Kategorie: 
Fachspezifische Veranstaltung
Art der Veranstaltung: 
Seminar/Workshop
Veranstalter: 
Graduate Center of Life Sciences
Verantwortung für Veranstaltung: 
Hauptverantwortung
Durchführung/Format: 
In Präsenz
Sprache: 
EN
Maximale Teilnehmendenzahl: 
20
Umfang in Stunden: 
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.