Add mattermost links

episodes/02-ppp.md

@@ -28,6 +28,13 @@ This activity offers a variety of exercises designed to help undergraduates unde
 
 The [Particle Physics Playground](https://sites.google.com/siena.edu/particle-physics-playground/home) provides an engaging and interactive way for participants to delve into the core principles of particle physics. By working with real CMS Open Data, students will enhance their theoretical knowledge through hands-on experience.
 
+::::::::::::::::::::::::::::: callout
+## Discussion forums
+
+Make sure to join the [Mattermost channel](https://mattermost.web.cern.ch/cmsodws2024/channels/2-particle-physics-playground) for this activity to engage directly with the workshop instructors and fellow participants.
+
+:::::::::::::::::::::::::::::
+
 ## Pre-learning Lesson
 
 Participants are encouraged to review the [Particle Physics Primer pre-learning lesson](https://cms-opendata-workshop.github.io/workshop2024-lesson-particle-physics-primer/instructor/index.html). This foundational lesson is equipped with lectures and exercises covering the following topics:
@@ -56,11 +63,10 @@ Participants will be introduced to various tools and techniques used in particle
    - Calculating masses using 4-vectors and creating histograms.
    - Discovering new particles by analyzing decay products.
 
-4. **Discuss and Collaborate**: Use discussion forums or collaborative platforms to share your findings, ask questions, and work with peers.
+4. **Discuss and Collaborate**: Use discussion forums ([Mattermost channel]()) or collaborative platforms to share your findings, ask questions, and work with peers.
 
 ### Additional Resources
 
-
 - **Particle Physics Primer Videos**: Watch public-oriented videos for a broad overview.
 - **Standard Model References**: Review materials on the Standard Model and its historical development.
 - **Advanced Lectures**: For those interested, watch the remaining lectures covering neutrino physics and dark matter.
@@ -69,6 +75,7 @@ Participants will be introduced to various tools and techniques used in particle
 ## You Have Choices!
 
 While Python and Jupyter notebooks are the primary tools for this activity, feel free to explore other tools and file formats that suit your needs. The goal is to learn and apply particle physics analysis techniques in a way that works best for you.
+
 :::::::::::::::::::::::::::::
 
 ::::::::::::::::::::::::::::::::::::: keypoints 

episodes/03-pdl.md

@@ -62,6 +62,13 @@ Uncertainty propagation concepts are included at each step of the analysis. Afte
    - Follow the instructions in the notebook to complete the analysis interactively. You will perform tasks such as plotting histograms, fitting data, and analyzing uncertainties.
 
 
+::::::::::::::::::::::::::::: callout
+## Discussion forums
+
+Make sure to join the [Mattermost channel](https://mattermost.web.cern.ch/cmsodws2024/channels/01-particle-discovery-lab) for this activity to engage directly with the workshop instructors and fellow participants.
+
+:::::::::::::::::::::::::::::
+
 ::::::::::::::::::::::::::::: callout
 ## Visualize with CMS Spy WebGL
 

episodes/04-ml-1.md

@@ -31,6 +31,13 @@ Before diving into ML in HEP, participants should have a basic understanding of:
 - Data handling and visualization
 - Elementary statistical concepts (mean, variance, etc.)
 
+::::::::::::::::::::::::::::: callout
+## Discussion forums
+
+Make sure to join the [Mattermost channel](https://mattermost.web.cern.ch/cmsodws2024/channels/3-machine-learning-in-hep) for this activity to engage directly with the workshop instructors and fellow participants.
+
+:::::::::::::::::::::::::::::
+
 ## Let's get the basics clear 
 
 [Machine learning](https://www.ibm.com/topics/machine-learning) (ML) is a branch of artificial intelligence (AI) and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy. If that is not clear, please watch [this video](https://www.youtube.com/watch?v=4RixMPF4xis).

episodes/05-ml-2.md

@@ -24,6 +24,14 @@ exercises: 0
 
 Machine learning techniques, such as Convolutional Neural Networks (CNNs) and autoencoders, play pivotal roles in analyzing particle physics data. This section provides insights into their architectures, training processes, and practical applications within the field.
 
+
+::::::::::::::::::::::::::::: callout
+## Discussion forums
+
+Make sure to join the [Mattermost channel](https://mattermost.web.cern.ch/cmsodws2024/channels/3-machine-learning-in-hep) for this activity to engage directly with the workshop instructors and fellow participants.
+
+:::::::::::::::::::::::::::::
+
 ## Supervised Learning - Convolutional Neural Networks (CNNs) 
 
 #### Purpose and Architecture

episodes/06-agc.md

@@ -43,6 +43,12 @@ Participants will work on a cross-section measurement using 2015 CMS Open Data.
 
 Using older CMS data presents unique challenges, such as analyzing data formats within the CMSSW software framework. Participants will learn strategies for overcoming these obstacles, ensuring their analyses are robust and accurate even when working with older CMS Open Data.
 
+::::::::::::::::::::::::::::: callout
+## Discussion forums
+
+Make sure to join the [Mattermost channel](https://mattermost.web.cern.ch/cmsodws2024/channels/4-advanced-generative-challenge) for this activity to engage directly with the workshop instructors and fellow participants.
+
+:::::::::::::::::::::::::::::
 
 ### Reproducibility and Scalability