Update course book

NeuromatchAcademy · Dec 5, 2023 · 5b15ece · 5b15ece
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diff --git a/.buildinfo b/.buildinfo
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 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: a191cef158ca8665d684bbe58bbd4944
+config: 1d4a1935b67cccc1c42ac01a9e6bf7f4
 tags: 645f666f9bcd5a90fca523b33c5a78b7
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diff --git a/_images/W3D5_Tutorial1_69_0.png b/_images/W3D5_Tutorial1_69_0.png
diff --git a/_images/W3D5_Tutorial2_30_0.png b/_images/W3D5_Tutorial2_30_0.png
diff --git a/_images/W3D5_Tutorial2_42_0.png b/_images/W3D5_Tutorial2_42_0.png
diff --git a/_images/W3D5_Tutorial2_53_6.png b/_images/W3D5_Tutorial2_53_6.png
diff --git a/_images/W3D5_Tutorial3_46_0.png b/_images/W3D5_Tutorial3_46_0.png
diff --git a/_images/W3D5_Tutorial3_53_30.png → _images/W3D5_Tutorial3_53_18.png b/_images/W3D5_Tutorial3_53_30.png → _images/W3D5_Tutorial3_53_18.png
diff --git a/_sources/tutorials/Schedule/daily_schedules.md b/_sources/tutorials/Schedule/daily_schedules.md
@@ -1,4 +1,5 @@
 # General schedule
+# Pilot Test
 
 ## Course overview
 

diff --git a/_sources/tutorials/W1D2_ModelFitting/student/W1D2_Tutorial3.ipynb b/_sources/tutorials/W1D2_ModelFitting/student/W1D2_Tutorial3.ipynb
@@ -27,7 +27,7 @@
     "\n",
     "**Content reviewers**: Lina Teichmann, Saeed Salehi, Patrick Mineault, Ella Batty, Michael Waskom\n",
     "\n",
-    "**Production editors:** Spiros Chavlis"
+    "**Production editors:** Spiros Chavlis\n"
    ]
   },
   {
@@ -774,7 +774,7 @@
     "*Estimated timing of tutorial: 23 minutes*\n",
     "\n",
     "- Bootstrapping is a resampling procedure that allows to build confidence intervals around inferred parameter values\n",
-    "- it is a widely applicable and very practical method that relies on computational power and pseudo-random number generators (as opposed to more classical approaches than depend on analytical derivations)"
+    "- it is a widely applicable and very practical method that relies on computational power and pseudo-random number generators (as opposed to more classical approaches that depend on analytical derivations)"
    ]
   },
   {

diff --git a/_sources/tutorials/W1D3_GeneralizedLinearModels/student/W1D3_Tutorial2.ipynb b/_sources/tutorials/W1D3_GeneralizedLinearModels/student/W1D3_Tutorial2.ipynb
@@ -247,7 +247,7 @@
     "  best_C = C_values[np.argmax(accuracies)]\n",
     "  ax.set(\n",
     "      xticks=C_values,\n",
-    "      xlabel=\"$C$\",\n",
+    "      xlabel=\"C\",\n",
     "      ylabel=\"Cross-validated accuracy\",\n",
     "      title=f\"Best C: {best_C:1g} ({np.max(accuracies):.2%})\",\n",
     "  )\n",
@@ -261,7 +261,7 @@
     "  ax.plot(C_values, non_zero_l1, marker=\"o\")\n",
     "  ax.set(\n",
     "    xticks=C_values,\n",
-    "    xlabel=\"$C$\",\n",
+    "    xlabel=\"C\",\n",
     "    ylabel=\"Number of non-zero coefficients\",\n",
     "  )\n",
     "  ax.axhline(n_voxels, color=\".1\", linestyle=\":\")\n",
@@ -725,7 +725,7 @@
     "\n",
     "*Estimated timing to here from start of tutorial: 30 min*\n",
     "\n",
-    "Now we need to evaluate the model's predictions. We'll do that with an *accuracy* score. The accuracy of the classifier is the proportion of trials where the predicted label matches the true label.\n"
+    "Now, we need to evaluate the predictions of the model. We will use an *accuracy* score for this purpose. The accuracy of a classifier is determined by the proportion of correct trials, where the predicted label matches the true label, out of the total number of trials."
    ]
   },
   {
@@ -794,7 +794,7 @@
     "execution": {}
    },
    "source": [
-    "[*Click for solution*](https://github.com/NeuromatchAcademy/course-content/tree/main/tutorials/W1D3_GeneralizedLinearModels/solutions/W1D3_Tutorial2_Solution_bfe654b0.py)\n",
+    "[*Click for solution*](https://github.com/NeuromatchAcademy/course-content/tree/main/tutorials/W1D3_GeneralizedLinearModels/solutions/W1D3_Tutorial2_Solution_1485808c.py)\n",
     "\n"
    ]
   },
@@ -933,7 +933,7 @@
    },
    "outputs": [],
    "source": [
-    "X.shape"
+    "print(X.shape)"
    ]
   },
   {
@@ -1211,10 +1211,10 @@
     "  penalized_models[log_C] = m.fit(X, y)\n",
     "\n",
     "@widgets.interact\n",
-    "def plot_observed(log_C = widgets.FloatSlider(value=1, min=1, max=10, step=1)):\n",
+    "def plot_observed(log_C = widgets.IntSlider(value=1, min=1, max=10, step=1)):\n",
     "  models = {\n",
     "    \"No regularization\": log_reg,\n",
-    "    f\"$L_2$ (C = $10^{log_C}$)\": penalized_models[log_C]\n",
+    "    f\"$L_2$ (C = $10^{{{log_C}}}$)\": penalized_models[log_C]\n",
     "  }\n",
     "  plot_weights(models)"
    ]
@@ -1225,7 +1225,7 @@
     "execution": {}
    },
    "source": [
-    "Recall from above that $C=\\frac1\\beta$ so larger `C` is less regularization. The top panel corresponds to $C=\\infty$."
+    "Recall from above that $C=\\frac1\\beta$ so larger $C$ is less regularization. The top panel corresponds to $C \\rightarrow \\infty$."
    ]
   },
   {
@@ -1453,7 +1453,7 @@
     "execution": {}
    },
    "source": [
-    "Smaller `C` (bigger $\\beta$) leads to sparser solutions.\n",
+    "Smaller $C$ (bigger $\\beta$) leads to sparser solutions.\n",
     "\n",
     "**Link to neuroscience**: When is it OK to assume that the parameter vector is sparse? Whenever it is true that most features don't affect the outcome. One use-case might be decoding low-level visual features from whole-brain fMRI: we may expect only voxels in V1 and thalamus should be used in the prediction.\n",
     "\n",
@@ -1647,7 +1647,7 @@
     "\n",
     "**The logistic link function**\n",
     "\n",
-    "You've seen $\\theta^T x_i$ before, but the $\\sigma$ is new. It's the *sigmoidal* or *logistic* link function that \"squashes\" $\\theta^T x_i$ to keep it between $0$ and $1$:\n",
+    "You've seen $\\theta^T x_i$ before, but the $\\sigma$ is new. It's the *sigmoidal* or *logistic* link function that \"squashes\" $\\theta^\\top x_i$ to keep it between $0$ and $1$:\n",
     "\n",
     "\\begin{equation}\n",
     "\\sigma(z) = \\frac{1}{1 + \\textrm{exp}(-z)}\n",
@@ -1753,7 +1753,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,

diff --git a/_sources/tutorials/W1D4_DimensionalityReduction/student/W1D4_Tutorial1.ipynb b/_sources/tutorials/W1D4_DimensionalityReduction/student/W1D4_Tutorial1.ipynb
@@ -27,7 +27,7 @@
     "\n",
     "**Content reviewers:** Roozbeh Farhoudi, Matt Krause, Spiros Chavlis, Richard Gao, Michael Waskom, Siddharth Suresh, Natalie Schaworonkow, Ella Batty\n",
     "\n",
-    "**Production editors:** Spiros Chavlis"
+    "**Production editors:** Gagana B, Spiros Chavlis"
    ]
   },
   {
@@ -971,7 +971,7 @@
     "  # Normalize vector u\n",
     "  u = ...\n",
     "\n",
-    "  # Calculate vector w that is orthogonal to w\n",
+    "  # Calculate vector w that is orthogonal to u\n",
     "  w = ...\n",
     "\n",
     "  # Put in matrix form\n",
@@ -1007,11 +1007,11 @@
     "execution": {}
    },
    "source": [
-    "[*Click for solution*](https://github.com/NeuromatchAcademy/course-content/tree/main/tutorials/W1D4_DimensionalityReduction/solutions/W1D4_Tutorial1_Solution_25e1d102.py)\n",
+    "[*Click for solution*](https://github.com/NeuromatchAcademy/course-content/tree/main/tutorials/W1D4_DimensionalityReduction/solutions/W1D4_Tutorial1_Solution_590fa120.py)\n",
     "\n",
     "*Example output:*\n",
     "\n",
-    "<img alt='Solution hint' align='left' width=379.0 height=378.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/course-content/main/tutorials/W1D4_DimensionalityReduction/static/W1D4_Tutorial1_Solution_25e1d102_0.png>\n",
+    "<img alt='Solution hint' align='left' width=379.0 height=378.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/course-content/main/tutorials/W1D4_DimensionalityReduction/static/W1D4_Tutorial1_Solution_590fa120_0.png>\n",
     "\n"
    ]
   },

diff --git a/_sources/tutorials/W2D2_LinearSystems/student/W2D2_Tutorial2.ipynb b/_sources/tutorials/W2D2_LinearSystems/student/W2D2_Tutorial2.ipynb
@@ -216,8 +216,8 @@
     "\n",
     "def plot_state_probabilities(time, states):\n",
     "  plt.figure()\n",
-    "  plt.plot(time, states[:, 0], label='Closed to open')\n",
-    "  plt.plot(time, states[:, 1], label='Open to closed')\n",
+    "  plt.plot(time, states[:, 0], label='Closed')\n",
+    "  plt.plot(time, states[:, 1], label='Open')\n",
     "  plt.xlabel('time')\n",
     "  plt.ylabel('prob(open OR closed)')\n",
     "  plt.legend()\n",
@@ -853,12 +853,12 @@
     "dt = 0.1   # timestep of our simulation\n",
     "\n",
     "# same parameters as above\n",
-    "# c2o: closed to open rate\n",
-    "# o2c: open to closed rate\n",
-    "c2o = 0.02\n",
-    "o2c = 0.1\n",
-    "A = np.array([[1 - c2o*dt, o2c*dt],\n",
-    "              [c2o*dt,     1 - o2c*dt]])\n",
+    "# c: closed rate\n",
+    "# o: open rate\n",
+    "c = 0.02\n",
+    "o = 0.1\n",
+    "A = np.array([[1 - c*dt, o*dt],\n",
+    "              [c*dt,     1 - o*dt]])\n",
     "\n",
     "# Initial condition: start as Closed\n",
     "x0 = np.array([[1, 0]])\n",
@@ -877,11 +877,11 @@
     "execution": {}
    },
    "source": [
-    "[*Click for solution*](https://github.com/NeuromatchAcademy/course-content/tree/main/tutorials/W2D2_LinearSystems/solutions/W2D2_Tutorial2_Solution_67f681fa.py)\n",
+    "[*Click for solution*](https://github.com/NeuromatchAcademy/course-content/tree/main/tutorials/W2D2_LinearSystems/solutions/W2D2_Tutorial2_Solution_41ec6e01.py)\n",
     "\n",
     "*Example output:*\n",
     "\n",
-    "<img alt='Solution hint' align='left' width=778.0 height=577.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/course-content/main/tutorials/W2D2_LinearSystems/static/W2D2_Tutorial2_Solution_67f681fa_0.png>\n",
+    "<img alt='Solution hint' align='left' width=778.0 height=577.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/course-content/main/tutorials/W2D2_LinearSystems/static/W2D2_Tutorial2_Solution_41ec6e01_0.png>\n",
     "\n"
    ]
   },