add info about winter/summer term in lecturesx

_teaching/summer_dgm.md

@@ -10,4 +10,5 @@ Generative Adversarial Networks (GANs) and similar methods (e.g. Diffusion Model
 Furthermore, their ability to synthesize images at unprecedented levels of realism also gives hope that the chronic scarcity of labeled data in the medical field can be resolved with the help of these generative models.
 This class starts with basic statistical concepts of image generation.
 We proceed with several lectures about contemporary deep learning approaches for image generation, such as Diffusion Models, Neural Cellular Automata, VAEs and GANs.
-As this is an *integrated* lecture, students can participate in a voluntary paper presentation and/or programming project to gain practical experience and an exam bonus.
+As this is an *integrated* lecture, students can participate in a voluntary paper presentation and/or programming project to gain practical experience and an exam bonus.
+The lecture takes place in every **summer** term.

_teaching/winter_dlmb.md

@@ -10,4 +10,5 @@ Medical imaging is the technique of creating visual representations (digital ima
 Oftentimes visual representation of the function of some organs or tissues is part of medical imaging.
 This class teaches the basics of common medical imaging modalities and discusses their strengths and weaknesses.
 It proceeds with several lectures about advanced concepts in deep learning (e.g. federated learning, continual learning) and their applications in the field of medical imaging.
-As this is an *integrated* lecture, students can participate in a voluntary paper presentation and/or programming project to gain practical experience and an exam bonus.
+As this is an *integrated* lecture, students can participate in a voluntary paper presentation and/or programming project to gain practical experience and an exam bonus.
+The lecture takes place in every **winter** term.