diff --git a/AA2324/course/02_math_recap_linear_algebra/02_math_recap_linear_algebra_BACKUP_801.ipynb b/AA2324/course/02_math_recap_linear_algebra/02_math_recap_linear_algebra_BACKUP_801.ipynb
deleted file mode 100644
index 2ee351c..0000000
--- a/AA2324/course/02_math_recap_linear_algebra/02_math_recap_linear_algebra_BACKUP_801.ipynb
+++ /dev/null
@@ -1,3088 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "# Artificial Intelligence and Machine Learning \n",
- "\n",
- "\n",
- "## Unit II \n",
- "### The geometry of linear maps\n",
- "**Iacopo Masi**\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "## 📚 Textbooks \n",
- "\n",
- "\n",
- "- The course is inspired and follows [CS229 by Stanford](http://cs229.stanford.edu/syllabus-summer2020.html) while other material is inspired from other courses \n",
- "\n",
- "There is not a single textbook but suggested are:\n",
- "\n",
- "| Topic | Authors | Book |\n",
- "| :-------------: |:-------------:|:-------------:|\n",
- "| Generic ML | H. Daumé III | \"A Course in Machine Learning\", [download the book](http://ciml.info/) |\n",
- "| Generic ML | Christopher M. Bishop | “Pattern Recognition and Machine Learning” [download the book](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf) |\n",
- "| Generic ML | Kevin P. Murphy | “Probabilistic Machine Learning: An introduction\", MIT Press, 2021 |\n",
- "| Deep Learning | Ian Goodfellow and Yoshua Bengio and Aaron Courville | “Deep Learning”, MIT Press 2016 |\n",
- "| Deep Learning | Ston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola | **[“Dive into Deep Learning”](https://d2l.ai/)** |\n",
- "\n",
- "**You can find online most of these or part of them.**"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "# Recap on Linear Algebra\n",
- "\n",
- "- [This pdf covers this part](http://cs229.stanford.edu/summer2019/cs229-linalg.pdf)\n",
- "- [Illustrations and some math part are taken from d2l.ai, linear algebra](https://d2l.ai/chapter_preliminaries/linear-algebra.html)\n",
- "- [and from from d2l.ai, geometric linear algebra](https://d2l.ai/chapter_appendix-mathematics-for-deep-learning/geometry-linear-algebraic-ops.html)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "## Training set\n",
- "\n",
- "\\begin{equation}\n",
- " \\underbrace{\\{\\mathbf{x}_i,y_i\\}_{i=1}^N}_{\\text{known}} \\sim \\underbrace{\\mathcal{D}}_{\\text{unknown}}\n",
- "\\end{equation}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "## $\\mathbf{x}$ as a high-dimensional point in a vector space\n",
- "\n",
- "- $\\mathbf{x} \\in \\mathbb{R}^D$ is a vector in D-dimensional real-space.\n",
- "- All the vectors are identified by using another point that functions as **origin**, i.e. in $\\mathbf{O}=(0, 0, 0)$ in $\\mathbb{R}^3$.\n",
- "- Moreover, for this to work you need an **orthonormal set of basis vectors** on which you can express your vector.\n",
- "- $\\mathbf{\\vec{x}}$ is bold because it means it's a vector; we drop $\\vec{}$ for clarity.\n",
- "- $y$ is a scalar value (it is not bold)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "### Vectors are written column-wise\n",
- "\n",
- "$$\\mathbf{x} = \n",
- "\\begin{bmatrix}\n",
- "x_0, \\\\\n",
- "x_1, \\\\\n",
- "\\ldots, \\\\\n",
- "x_{D-1} \\\\\n",
- "\\end{bmatrix} $$\n",
- "\n",
- "### To make it row-wise just transpose it\n",
- "\n",
- "$$\\mathbf{x}^{T} = \\begin{bmatrix}\n",
- "x_0, & x_1, & \\ldots, x_{D-1} \\\\\n",
- "\\end{bmatrix} $$"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "## Numpy\n",
- "\n",
- "NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\n",
- "\n",
- "\n",
- "\n",
- "## During the course, we will learn how to \"vectorize\" the code (i.e. avoiding for loop)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[ 2.5 3.2]\n",
- " [ 0. 1. ]\n",
- " [ 2. -3. ]]\n",
- "Shape (3, 2)\n",
- "Number of dimension: 2\n",
- "Number of elements: 6\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "x = np.array([[2.5, 3.2], [0, 1], [2, -3]], dtype=np.float32)\n",
- "print(x)\n",
- "print(f\"Shape {x.shape}\") # the shape is...\n",
- "print(f\"Number of dimension: {x.ndim}\") # is a matrix (2 axis)\n",
- "print(f\"Number of elements: {x.size}\") # with 6 elements\n",
- "\n",
- "v = np.array([2.5, 3.2]) # used later "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "# Let's try to plot the vector\n",
- "\n",
- "$$\\mathbf{x}^{T} = \\begin{bmatrix} 2.5, 3.2 \\\\\\end{bmatrix} $$"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "```python\n",
- "import matplotlib\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "\n",
- "v = np.array([2.5, 3.2])\n",
- "# all the X first, then all the Y\n",
- "# [X1 X2] [Y1 Y2]\n",
- "plt.plot([0, v[0]], [0, v[1]], \n",
- " marker='x', color='red', lw=4,\n",
- " markersize=6)\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "hide_input": true,
- "scrolled": false,
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "outputs": [
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "