Improve structure of tutorial sections (#909)

Co-authored-by: nckw <[email protected]>

docs/part4/usefullinks.md

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+hide:
+    - navigation 
+
 # Useful links and further reading
 
 ### Tutorials and reading material

docs/part5/longexercise.md

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-# Long exercise: main features of Combine
+# Main Features of Combine (Long Exercises)
 This exercise is designed to give a broad overview of the tools available for statistical analysis in CMS using the combine tool. <span style="font-variant:small-caps;">Combine</span> is a high-level tool for building `RooFit`/`RooStats` models and running common statistical methods. We will cover the typical aspects of setting up an analysis and producing the results, as well as look at ways in which we can diagnose issues and get a deeper understanding of the statistical model. This is a long exercise - expect to spend some time on it especially if you are new to <span style="font-variant:small-caps;">Combine</span>. If you get stuck while working through this exercise or have questions specifically about the exercise, you can ask them on [this mattermost channel](https://mattermost.web.cern.ch/cms-exp/channels/hcomb-tutorial). Finally, we also provide some solutions to some of the questions that are asked as part of the exercise. These are available [here](https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part5/longexerciseanswers).
 
 For the majority of this course we will work with a simplified version of a real analysis, that nonetheless will have many features of the full analysis. The analysis is a search for an additional heavy neutral Higgs boson decaying to tau lepton pairs. Such a signature is predicted in many extensions of the standard model, in particular the minimal supersymmetric standard model (MSSM). You can read about the analysis in the paper [here](https://arxiv.org/pdf/1803.06553.pdf). The statistical inference makes use of a variable called the total transverse mass ($M_{\mathrm{T}}^{\mathrm{tot}}$) that provides good discrimination between the resonant high-mass signal and the main backgrounds, which have a falling distribution in this high-mass region. The events selected in the analysis are split into a several categories which target the main di-tau final states as well as the two main production modes: gluon-fusion (ggH) and b-jet associated production (bbH). One example is given below for the fully-hadronic final state in the b-tag category which targets the bbH signal:
@@ -718,3 +718,5 @@ For a model with two POIs it is often useful to look at the how well we are able
 
   - Run a 2D likelihood scan in `r_ggH` and `r_bbH`. You can start with around 100 points but may need to increase this later too see more detail in the resulting plot.
   - Have a look at the output limit tree, it should have branches for each POI as well as the usual deltaNLL value. You can use TTree::Draw to plot a 2D histogram of deltaNLL with `r_ggH` and `r_bbH` on the axes.
+
+

docs/part5/longexerciseanswers.md

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-# Answers to tasks and questions in long exercise
+# Answers to tasks and questions
 
 ## Part 1: A one-bin counting experiment
 
@@ -249,6 +249,3 @@ python plot1DScan.py higgsCombine.part3E.MultiDimFit.mH200.root --others 'higgsC
 </details>
 
 
-### F: Use of channel masking
-
-No specific questions, just tasks

docs/part5/roofit.md

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-# RooFit
+# RooFit Basics
 `RooFit` is a OO analysis environment built on `ROOT`. It has a collection of classes designed to augment root for data modeling.
 
 This section covers a few of the basics of `RooFit`. There are many more tutorials available at this link: [https://root.cern.ch/root/html600/tutorials/roofit/index.html](https://root.cern.ch/root/html600/tutorials/roofit/index.html)

docs/tutorial2023/parametric_exercise.md

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-# Parametric fitting exercise
+# Parametric Models in Combine
 
 ## Getting started
 By now you should have a working setup of Combine v9 from the pre-tutorial exercise. If so then move onto the cloning of the parametric fitting exercise gitlab repo below. If not then you need to set up a CMSSW area and checkout the combine package:

docs/tutorial2023_unfolding/unfolding_exercise.md

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-# Combine unfolding exercise
+# Likelihood Based Unfolding Exercise in Combine
 
 ## Getting started
 

mkdocs.yml

@@ -17,13 +17,14 @@ nav:
           - "Unfolding & regularization": part3/regularisation.md
           - "Validating datacards":       part3/validation.md
           - "Debugging fit failures":     part3/debugging.md
-        - Links & FAQ: part4/usefullinks.md
         - Tutorials:
           - "RooFit Basics":              part5/roofit.md
-          - "Exercise: main features":    part5/longexercise.md
-          - "Solutions to long exercise": part5/longexerciseanswers.md
-          - "Exercise: parametric fit":   tutorial2023/parametric_exercise.md
-          - "Exercise: unfolding in combine":   tutorial2023_unfolding/unfolding_exercise.md
+          - "Main Features":                
+            - "Exercises":                part5/longexercise.md
+            - "Solutions":                part5/longexerciseanswers.md
+          - "Parametric Models":          tutorial2023/parametric_exercise.md
+          - "Likelihood Based Unfolding": tutorial2023_unfolding/unfolding_exercise.md
+        - Links & FAQ: part4/usefullinks.md
 
 theme:
         name: material
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         features: 
             - content.code.copy
             - navigation.footer
-            - navigation.expand
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-            - navigation.sections
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