Merge pull request #111 from SmashingBumpkin/main

Small changes to allow correct parsing, exam dates

AA2324/course/06_clustering_gaussian_MLE/06_clustering_gaussian_MLE.ipynb

@@ -828,7 +828,7 @@
    "source": [
     "# The Maximum Likelihood Principle\n",
     "\n",
-    "This has a Bayesian interpretation which can be helpful to think about.  Suppose that we have a model with parameters $\\boldsymbol{\\theta}\\doteq\\mu,\\Sigma$ and a collection of data examples $X=\\{\\mbf{x}_1,\\ldots,\\mbf{x}_N \\}$. \n",
+    "This has a Bayesian interpretation which can be helpful to think about.  Suppose that we have a model with parameters $\\boldsymbol{\\theta}\\doteq\\mu,\\Sigma$ and a collection of data examples $X=\\{\\mbf{x}_1,\\ldots,\\mbf{x}_N \\}$.\n",
     "\n",
     "If we want to find the **most likely value for the parameters of our model, given the data**, that means we want to find\n",
     "\n",

AA2324/course/11_regression_lsq_poly/11_regression_lsq_poly.ipynb

@@ -2152,7 +2152,8 @@
     "# Gradient Descent and [Stochastic] GD\n",
     "\n",
     "1. **Initialization - Very Important if the function is not strictly convex** \n",
-    "$$\\bmf{\\theta} \\doteq \\mbf{0}^T$$ Set it to all zeros or random initialization from a distribution.\n",
+    "$$\\bmf{\\theta} \\doteq \\mbf{0}^T$$\n",
+    "Set it to all zeros or random initialization from a distribution.\n",
     "2. Repeat until **convergence**:\n",
     "    - Compute the gradient of the loss wrt the parameters $\\bmf{\\theta}$ given **all the training set**\n",
     "    - Take a small step in the opposite direction of steepest ascent **(so steepest descent).**<br/><br/>\n",
@@ -2750,7 +2751,7 @@
     "# Now we can still solve it with LS but $m=2$\n",
     "\n",
     "\n",
-    "We can have another dimensionality $m$ instead of $d$ by using **Basis Functions $\\bmf{\\phi}(\\mbf{x})$**.\n",
+    "We can have another dimensionality $m$ instead of $d$ by using **Basis Functions** $\\bmf{\\phi}(\\mbf{x})$ .\n",
     "\n",
     "With $\\bmf{\\phi}(\\mbf{x} = [1,\\phi(x_1),\\ldots,\\phi(x_m)]$ and $\\mbf{\\theta} = [\\theta_0,\\theta_1,\\ldots,\\theta_m]$, we have:\n",
     "\n",
@@ -2837,7 +2838,7 @@
     "# Now we can still solve it with LS but $m=3$\n",
     "\n",
     "\n",
-    "We can have another dimensionality $m$ instead of $d$ by using **Basis Functions $\\bmf{\\phi}(\\mbf{x})$**.\n",
+    "We can have another dimensionality $m$ instead of $d$ by using **Basis Functions** $\\bmf{\\phi}(\\mbf{x})$ .\n",
     "\n",
     "With $\\bmf{\\phi}(\\mbf{x} = [1,\\phi(x_1),\\ldots,\\phi(x_m)]$ and $\\mbf{\\theta} = [\\theta_0,\\theta_1,\\ldots,\\theta_m]$, we have:\n",
     "\n",
@@ -2924,7 +2925,7 @@
     "# We can analyze what happens in function of  $m$\n",
     "\n",
     "\n",
-    "We can have another dimensionality $m$ instead of $d$ by using **Basis Functions $\\bmf{\\phi}(\\mbf{x})$**.\n",
+    "We can have another dimensionality $m$ instead of $d$ by using **Basis Functions** $\\bmf{\\phi}(\\mbf{x})$.\n",
     "\n",
     "With $\\bmf{\\phi}(\\mbf{x} = [1,\\phi(x_1),\\ldots,\\phi(x_m)]$ and $\\mbf{\\theta} = [\\theta_0,\\theta_1,\\ldots,\\theta_m]$, we have:\n",
     "\n",

AA2324/course/14_neural_nets_backprop/14_neural_nets_backprop.ipynb

@@ -634,7 +634,11 @@
     "\n",
     "\n",
     "1. **<ins>Initialization - Very Important if the function is not strictly convex</ins>** \n",
-    "$\\bmf{\\theta} \\sim \\mathcal{N}(\\cdot)~~~\\text{omit details for now}$$ With NN random initialization from a distribution (There are different methods). **We do not set them all to zero**\n",
+    "\n",
+    "$$\\bmf{\\theta} \\sim \\mathcal{N}(\\cdot)~~~\\text{omit details for now}$$\n",
+    "\n",
+    "With NN random initialization from a distribution (There are different methods). **We do not set them all to zero**\n",
+    "\n",
     "2. Repeat until **convergence**:\n",
     "    - Compute the gradient of the loss wrt the parameters $\\bmf{\\theta}$ given **the mini-batch**\n",
     "    - Take a small step in the opposite direction of steepest ascent **(so steepest descent).**<br/><br/>\n",
@@ -1775,7 +1779,7 @@
    "source": [
     "# Universal Approximation Theorem [Informal]\n",
     "\n",
-    "Given a continuous function $\\mbf{y}=f(\\mbf{x})$ where $\\mbf{x} \\in \\mathbb{R}^d$ and $\\mbf{y} \\in \\mathbb{R}^k$, considering only a bounded region of $\\mbf{x}$, **there exists** a single-hidden-layer NN$_\\theta$ with a **finite number of neurons/units in the hidden layer**, such that:\n",
+    "Given a continuous function $\\mbf{y}=f(\\mbf{x})$ where $\\mbf{x} \\in \\mathbb{R}^d$ and $\\mbf{y} \\in \\mathbb{R}^k$, considering only a bounded region of $\\mbf{x}$, **there exists** a single-hidden-layer $NN_\\theta$ with a **finite number of neurons/units in the hidden layer**, such that:\n",
     "\n",
     "$$\\vert f(\\mbf{x}) -  NN_\\theta(\\mbf{x}) \\vert \\le \\epsilon $$\n",
     "<br><br>\n",

AA2324/course/15_backprop_jacobians/15_backprop_jacobians.ipynb

@@ -3179,11 +3179,11 @@
    },
    "source": [
     "# 🏁 END of the LINE 🏁\n",
-    "### Soon it will be your turn on 13 June 2023\n",
+    "### Soon it will be your turn on 13 June 2024\n",
     "\n",
     "## Do not worry there are also\n",
-    "- Exam session on **6 July 2023**\n",
-    "- Exam session on **14 September 2023**\n",
+    "- Exam session on **16 July 2024**\n",
+    "- Exam session on **18 September 2024**\n",
     "\n",
     "[Million Dollar 🤑  link](https://iacopomasi.github.io/AI-ML-Unit-2/AA2122/exams.html)"
    ]