add dates to teaching

_config.yml

@@ -241,6 +241,8 @@ defaults:
     values:
       layout: single
       author_profile: true
+      share: false
+      comments: false
   # _posts
   - scope:
       path: ""

_pages/team.html

@@ -3,8 +3,6 @@
 title: "Team"
 permalink: /team/
 author_profile: true
-redirect_from:
-  - /we
 ---
 
 {% include base_path %}

_teaching/summer_dgm.md

@@ -3,6 +3,8 @@ title: "Deep Generative Models"
 collection: teaching
 type: "Undergraduate course"
 permalink: /teaching/summer_dgm
+date: 2024-09-23
 ---
 
-Generative Adversarial Networks (GANs) and similar methods (e.g. Diffusion Models, Variational Auto Encoders) have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image denoising, reconstruction, segmentation, data simulation, detection or classification. 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.
+Generative Adversarial Networks (GANs) and similar methods (e.g. Diffusion Models, Variational Auto Encoders) have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image denoising, reconstruction, segmentation, data simulation, detection or classification.
+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.

_teaching/winter_dlmb.md

@@ -3,6 +3,7 @@ title: "Deep Learning for Medical Imaging"
 collection: teaching
 type: "Undergraduate course"
 permalink: /teaching/winter_dlmb
+date: 2024-09-23
 ---
 
 Medical imaging is the technique of creating visual representations (digital images) of the interior of a body or skin for clinical analysis and medical interventions.