BIOENG-450: In silico neuroscience"In silico Neuroscience" introduces students to a synthesis of modern neuroscience and state-of-the-art data management, modelling and computing technologies.
CS-401: Applied data analysisThis course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the data science world (pandas, scikit-learn, Spark, etc.)
DH-405: Foundations of digital humanitiesThis course gives an introduction to the fundamental concepts and methods of the Digital Humanities, both from a theoretical and applied point of view. The course introduces the Digital Humanities circle of processing and interpretation, from data acquisition to new understandings.
BIO-311: NeuroscienceThe course starts with fundamentals of electrical - and chemical signaling in neurons. Students then learn how neurons in the brain receive and process sensory information, and how other neurons control the behavior of an animal. Furthermore, memory, learning, and brain disorders will be introduced.
BIO-480: Neuroscience: from molecular mechanisms to diseaseThe goal of the course is to guide students through the essential aspects of molecular neuroscience and neurodegenerative diseases. The student will gain the ability to dissect the molecular basis of disease in the nervous system in order to begin to understand and identify therapeutic strategies.
DH-406: Machine learning for DHThis course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and implement methods to analyze diverse data types, such as images, music and social network data.
MICRO-428: MetrologyThe course deals with the concept of measuring in different domains, particularly in the electrical, optical, and microscale domains. The course will end with a perspective on quantum measurements, which could trigger the ultimate revolution in metrology.
BIOENG-455: Computational cell biologyComputer modelling is increasingly used to study dynamic phenomena in cell biology. This course shows how to identify common mathematical features in cell biological mechanisms, and become proficient in selecting numerical algorithms to model them and predict their behaviour.
CS-233(a): Introduction to machine learning (BA3)Machine learning and data analysis are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented.
BIO-212: Biological chemistry IBiochemistry is a key discipline for the Life Sciences. Biological Chemistry I and II are two tightly interconnected courses that aim to describe and understand in molecular terms the processes that make life possible.