Machine learning (ML) has become widespread in industry, technology and society in recent years. This spread has not even stopped at physics. In this lecture, the basics of modern ML are taught. The paradigms of supervised, self-supervised and unsupervised learning will be discussed. In addition to classical ML, we will also discuss the methods of Deep Learning in detail and conclude the lecture with generative approaches. The lecture aspires to deepen the subject taught using exercises and examples.
data set up-to-date
Scope:
lecture: 2 hours/week tutorials: 1 hours/week
Time/location:
FR(4) REC/C213
Tutorials:
Group
Time/location:
DO(4) gW. CHE/0184
Audience:
Vertiefung Bachelor (PV) und Master (alle)
Specialization area:
Teilchen- und Kernphysik (Vorlesung im Wahlpflichtvertiefungsgebiet, masterartig)
Previous knowledge:
Coding experience in python is beneficial, prior knowledge in applied statistics is also beneficial