Changes to episode timings and instructor notes

_episodes_rmd/01-introduction-to-high-dimensional-data.Rmd

@@ -2,7 +2,7 @@
 title: "Introduction to high-dimensional data"
 author: "GS Robertson"
 source: Rmd
-teaching: 20
+teaching: 30
 exercises: 20
 questions:
 - What are high-dimensional data and what do these data look like in the

_episodes_rmd/02-high-dimensional-regression.Rmd

@@ -1,8 +1,8 @@
 ---
 title: "Regression with many outcomes"
 source: Rmd
-teaching: 60
-exercises: 30
+teaching: 70
+exercises: 50
 questions:
 - "How can we apply linear regression in a high-dimensional setting?"
 - "How can we benefit from the fact that we have many outcomes?"

_episodes_rmd/03-regression-regularisation.Rmd

@@ -1,8 +1,8 @@
 ---
 title: "Regularised regression"
 source: Rmd
-teaching: 60
-exercises: 20
+teaching: 110
+exercises: 60
 questions:
 - "What is regularisation?"
 - "How does regularisation work?"

_episodes_rmd/04-principal-component-analysis.Rmd

@@ -3,7 +3,7 @@ title: "Principal component analysis"
 author: "GS Robertson"
 source: Rmd
 teaching: 90
-exercises: 30
+exercises: 40
 questions:
 - What is principal component analysis (PCA) and when can it be used?
 - How can we perform a PCA in R?

_episodes_rmd/05-factor-analysis.Rmd

@@ -2,7 +2,7 @@
 title: "Factor analysis"
 author: "GS Robertson"
 source: Rmd
-teaching: 25
+teaching: 30
 exercises: 10
 questions:
 - What is factor analysis and when can it be used?

_episodes_rmd/06-k-means.Rmd

@@ -1,8 +1,8 @@
 ---
 title: "K-means"
 source: Rmd
-teaching: 45
-exercises: 15
+teaching: 60
+exercises: 20
 questions:
   - How do we detect real clusters in high-dimensional data?
   - How does K-means work and when should it be used?

_episodes_rmd/07-hierarchical.Rmd

@@ -1,8 +1,8 @@
 ---
 title: "Hierarchical clustering"
 source: Rmd
-teaching: 60
-exercises: 10
+teaching: 70
+exercises: 20
 questions:
 - What is hierarchical clustering and how does it differ from other clustering methods?
 - How do we carry out hierarchical clustering in R?

_extras/guide.md

@@ -1,6 +1,26 @@
 ---
 title: "Instructor Notes"
 ---
-Coming soon.
+
+Thank you for teaching high dimensional statistics with R! We hope you enjoy teaching the lesson. This page contains additional information for instructors.
+
+The materials for each episode are self-contained and can be found through the episode links on the home page. The slides in the Extras section can be used to supplement these materials.
+
+In previous rounds of teaching, the lesson was taught in four sessions, each lasting 2 hours and 50 minutes. We also advise allowing around 40 minutes of additional time for breaks. The recommended timings for each session are as follows:
+
+Session 1: 
+- Introduction to high-dimensional data (episode 1): 30 minutes of teaching time, 20 minutes for exercises (total: 50 minutes).
+- Regression with many outcomes (episode 2): 70 minutes of teaching time, 50 minutes for exercises (total: 120 minutes).
+
+Session 2:
+- Regression with many outcomes (episode 3): 110 minutes of teaching time, 60 minutes for exercises (total: 170 minutes).
+
+Session 3:
+- Principal component analysis (episode 4): 90 minutes of teaching time, 40 minutes for exercises (total: 130 minutes).
+- Factor analysis (episode 5): 30 minutes of teaching time, 10 minutes for exercises (total: 40 minutes).
+
+Session 4:
+- K-means (episode 6): 60 minutes of teaching time, 20 minutes for exercises (total: 80 minutes).
+- Hierarchical clustering (episode 7): 70 minutes of teaching time, 20 minutes for exercises (total: 90 minutes).
 
 {% include links.md %}

reference.md

@@ -4,6 +4,5 @@ layout: reference
 
 ## Glossary
 
-Coming soon. Feel free to suggest entries via GitHub Issues!
 
 {% include links.md %}