Staging

tutorials/W1D3_ComparingArtificialAndBiologicalNetworks/W1D3_Intro.ipynb

@@ -61,7 +61,9 @@
     "\n",
     "Though it is not required, you will benefit from these tutorials more if you have had the opportunity to take linear algebra and multivariate statistics courses in the past, as most of the computations and expressed ideas use the tools from these disciplines.\n",
     "\n",
-    "Note that the intro video for this day is longer than usual; it is the only day that has this property - feel free to return to it later if you don't have enough time before taking the tutorials."
+    "The intro video for this day in the last iteration of the course (2024) was relatively long and for this year (2025) it has been split into two videos. Please watch the video below to guide you through the tutorials. The additional part of the video is available in the first bonus tutorial (Tutorial 4), which goes into more detail related to the topics you will cover if you complete Tutorials 1-3. You are also kindly advised to revisit the bonus tutorials in your own time. We have added a new (non-Bonus) tutorial all about the exciting topic of Dynamic Similarity Analysis in this year's iteration of the course. This complements the topic of geometric (spatial) similarity with further similarity measures that take into account temporal information.\n",
+    "\n",
+    "We hope you enjoy the topics today. We'll now move on to Heiko and Niko to guide you through today's material."
    ]
   },
   {
@@ -123,7 +125,7 @@
     "  return tab_contents\n",
     "\n",
     "\n",
-    "video_ids = [('Youtube', 'RGOB0LRnLME'), ('Bilibili', 'BV1Kb421p77j')]\n",
+    "video_ids = [('Youtube', 'a66lOCB4oFw'), ('Bilibili', 'BV12eVjz3EbK')]\n",
     "tab_contents = display_videos(video_ids, W=854, H=480)\n",
     "tabs = widgets.Tab()\n",
     "tabs.children = tab_contents\n",
@@ -208,7 +210,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.9.22"
   }
  },
  "nbformat": 4,

tutorials/W1D3_ComparingArtificialAndBiologicalNetworks/W1D3_Tutorial3.ipynb

@@ -23,9 +23,9 @@
     "\n",
     "__Content creators:__ Veronica Bossio, Eivinas Butkus, Jasper van den Bosch\n",
     "\n",
-    "__Content reviewers:__ Samuele Bolotta, Yizhou Chen, RyeongKyung Yoon, Ruiyi Zhang, Lily Chamakura, Patrick Mineault, Hlib Solodzhuk\n",
+    "__Content reviewers:__ Samuele Bolotta, Yizhou Chen, RyeongKyung Yoon, Ruiyi Zhang, Lily Chamakura, Patrick Mineault, Hlib Solodzhuk, Alex Murphy\n",
     "\n",
-    "__Production editors:__ Konstantine Tsafatinos, Ella Batty, Spiros Chavlis, Samuele Bolotta, Hlib Solodzhuk, Patrick Mineault\n"
+    "__Production editors:__ Konstantine Tsafatinos, Ella Batty, Spiros Chavlis, Samuele Bolotta, Hlib Solodzhuk, Patrick Mineault, Alex Murphy\n"
    ]
   },
   {
@@ -2360,6 +2360,17 @@
     "\n",
     "5. Addressed two sources of model-performance estimation error that statistical inference must account for in addition to the error due to measurement noise: stimulus sampling and subject sampling, using the 2-factor bootstrap method."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "# The Big Picture\n",
+    "\n",
+    "Generalization can arise across multiple dimensions, whether that be generalization of an experiment to a new set of subjects or whether a new set of stimuli generalize to the same subjects. More likely, we would want to know how much an experiment would generalize to novel subjects across novel stimuli. We can test these ideas statistically by using the bootstreap method in statistics. This notebook highlights some issues with naive approaches to statistics when assessing generalization in this way. We explored the 2-factor bootstrap method and used a toolbox that explicitly takes care of the calculation so that we don't overestimate the variance involved. It's important to be aware of factors of generalization and how multiple overlapping factors might interact."
+   ]
   }
  ],
  "metadata": {
@@ -2390,7 +2401,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.9.22"
   }
  },
  "nbformat": 4,

tutorials/W1D3_ComparingArtificialAndBiologicalNetworks/instructor/W1D3_Intro.ipynb

@@ -61,7 +61,9 @@
     "\n",
     "Though it is not required, you will benefit from these tutorials more if you have had the opportunity to take linear algebra and multivariate statistics courses in the past, as most of the computations and expressed ideas use the tools from these disciplines.\n",
     "\n",
-    "Note that the intro video for this day is longer than usual; it is the only day that has this property - feel free to return to it later if you don't have enough time before taking the tutorials."
+    "The intro video for this day in the last iteration of the course (2024) was relatively long and for this year (2025) it has been split into two videos. Please watch the video below to guide you through the tutorials. The additional part of the video is available in the first bonus tutorial (Tutorial 4), which goes into more detail related to the topics you will cover if you complete Tutorials 1-3. You are also kindly advised to revisit the bonus tutorials in your own time. We have added a new (non-Bonus) tutorial all about the exciting topic of Dynamic Similarity Analysis in this year's iteration of the course. This complements the topic of geometric (spatial) similarity with further similarity measures that take into account temporal information.\n",
+    "\n",
+    "We hope you enjoy the topics today. We'll now move on to Heiko and Niko to guide you through today's material."
    ]
   },
   {
@@ -123,7 +125,7 @@
     "  return tab_contents\n",
     "\n",
     "\n",
-    "video_ids = [('Youtube', 'RGOB0LRnLME'), ('Bilibili', 'BV1Kb421p77j')]\n",
+    "video_ids = [('Youtube', 'a66lOCB4oFw'), ('Bilibili', 'BV12eVjz3EbK')]\n",
     "tab_contents = display_videos(video_ids, W=854, H=480)\n",
     "tabs = widgets.Tab()\n",
     "tabs.children = tab_contents\n",
@@ -208,7 +210,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.9.22"
   }
  },
  "nbformat": 4,