diff --git a/2025/labs/lab1/README.md b/2025/labs/lab1/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..7666716b316dc4a6785da7a4d925d48ca2a1c51c
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+++ b/2025/labs/lab1/README.md
@@ -0,0 +1,31 @@
+# Instructions
+
+First follow the instructions in NLU+ Coursework 1 and create the `nlu` conda environment. Then activate the environment, and install JupyterLab with pip:
+
+```shell
+pip install notebook
+```
+
+Once installed, launch Jupyter notebook with:
+```shell
+jupyter notebook
+```
+and open the `lab1.ipynb` file.
+
+
+### Alternative: Lab1-specific environment
+
+As an alternative, you can create and work with a virtual environment just for Lab1.
+Simply run:
+```shell
+conda create -n lab1 python=3.7
+```
+then, activate the environment and install the required packages:
+```shell
+conda activate lab1
+pip install numpy==1.21.5 scipy==1.7.3 notebook==6.4.8
+```
+and launch Jupyter notebook with:
+```shell
+jupyter notebook
+```
diff --git a/2025/labs/lab1/lab1.ipynb b/2025/labs/lab1/lab1.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..fcd22433c1d98dbacdeaca302c8c490a03bde5ab
--- /dev/null
+++ b/2025/labs/lab1/lab1.ipynb
@@ -0,0 +1,689 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "692b28a7-a120-4a05-b0c9-d3ed1399ce5d",
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ },
+ "tags": []
+ },
+ "source": [
+ "# NLU+ 2024-2025: Lab 1\n",
+ "#### Authors: Christos Baziotis, Lexi Birch, Frank Keller\n",
+ "These exercises aim to support you in successfully completing your assignments. Here, we will focus on NumPy, which is a library for working with numerical data in Python and is what you will be using in your first assignment. It would be helpful to first read the official NumPy quickstart [guide](https://numpy.org/doc/stable/user/absolute_beginners.html). While here we will review some operations that will help you with your assignment, you should use the official guide as a reference.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3d3209c9",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "np.set_printoptions(suppress=True) # suppresses the use of scientific notation for small numbers\n",
+ "\n",
+ "# you may use this function to print a numpy array and its properties\n",
+ "def print_array(arr):\n",
+ " print(arr)\n",
+ " print(\"shape:\", arr.shape)\n",
+ " print(\"type:\", arr.dtype.type)\n",
+ " print()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "40374a13",
+ "metadata": {},
+ "source": [
+ "# Load the data\n",
+ "We will be working with the Wine Data Set from UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Wine). It is contains the results of a chemical analysis of 178 wines. The wines are categorized into 3 classes and described by 13 attributes. All attributes are continuous.\n",
+ "\n",
+ "The dataset is stored in the `wine.csv` file. The first row contains the column names and the rest of the rows the corresponding values. Open the file and check its structure. The columns in the data are as follows:\n",
+ "\n",
+ " 1. *Type*: The type of wine, into one of three classes, 1 (59 obs), 2(71 obs), and 3 (48 obs).\n",
+ " 2. Alcohol\n",
+ " 3. Malic acid\n",
+ " 4. Ash\n",
+ " 5. Alcalinity of ash\n",
+ " 6. Magnesium\n",
+ " 7. Total phenols\n",
+ " 8. Flavanoids\n",
+ " 9. Nonflavanoid phenols\n",
+ " 10. Proanthocyanins\n",
+ " 11. Color intensity\n",
+ " 12. Hue\n",
+ " 13. D280/OD315 of diluted wines\n",
+ " 14. Proline\n",
+ "\n",
+ "**NOTE**: As you can see, the first attribute is the *class* identifier (1-3)\n",
+ "\n",
+ "\n",
+ "\n",
+ "First, we naively read all the data into a regular 2D Python list (i.e., list of lists), named `data`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cd548302",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# solution\n",
+ "data = []\n",
+ "with open(\"wine.csv\") as f:\n",
+ " for line in f:\n",
+ " row = line.strip().split(\",\")\n",
+ " data.append(row)\n",
+ " \n",
+ "print(data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6f138e6b",
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
+ "source": [
+ "### 1. Initialize a Numpy Array\n",
+ "Create a numpy array (named `data` ) out of the Python array and check its shape and data type.\n",
+ "What is the data type of the numpy array and why? How do numpy arrays differ from regular Python lists?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c0ec94d7",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1cb13efd",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# let's see what is in the array\n",
+ "print_array(data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4d572df0",
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
+ "source": [
+ "### 2. Array Indexing and Slicing\n",
+ "Now we need to split the data into separate numpy arrays, in order to disentangle the attribute names, attribute values and class labels. To do this you need to use numpy array slicing.\n",
+ "Do the following:\n",
+ " 1. Store the 13 attribute names into an 1D numpy array, called `names`. This means you should ignore the first column (i.e., Wine type).\n",
+ " 2. Store the class labels (i.e., Wine type) into an 1D numpy array, called `classes`.\n",
+ " 3. Store the attribute values into an 2D numpy array, called `attributes`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2712f977",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "65acf4d2",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "print(\"names\")\n",
+ "print_array(names)\n",
+ "\n",
+ "print(\"classes\")\n",
+ "print_array(classes)\n",
+ "\n",
+ "print(\"attributes\")\n",
+ "print_array(attributes)\n",
+ "\n",
+ "assert names.shape == (13,)\n",
+ "assert classes.shape == (178,)\n",
+ "assert attributes.shape == (178, 13)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "90ce761e",
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
+ "source": [
+ "4\\. Using the `attributes` array, print the second to last row, without its last 3 elements.\n",
+ "\n",
+ "The expected output is\n",
+ "`['13.17' '2.59' '2.37' '20' '120' '1.65' '.68' '.53' '1.46' '9.3']`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "384b4f64",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1fb4b28f",
+ "metadata": {},
+ "source": [
+ "### 3. Data Type Casting\n",
+ "Cast each numpy array to the appropriate data type. We need to represent numerical values with the appropriate data type to be able to do numerical operations.\n",
+ "1. The `attributes` array contains continuous values, therefore it needs to be converted to `float`.\n",
+ "2. The `classes` array contains categorical values, so you should convert it to `int`.\n",
+ "\n",
+ "(The `names` array already contains string values as it should. You don't need to change it.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "459fe022",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d9d77dd0",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "print(\"classes\")\n",
+ "print_array(classes)\n",
+ "\n",
+ "print(\"attributes\")\n",
+ "print_array(attributes)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cae988ab",
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
+ "source": [
+ "\n",
+ "### 4. Array Axis and Statistics\n",
+ "We often need to compute some statistics using aggregating methods. A common pitfall however is computing these statistics along the wrong axis.\n",
+ "\n",
+ "Using the `attributes` numpy array, do the following:\n",
+ "\n",
+ "1. Compute the sum of all values.\n",
+ "2. Compute the average value of each column (i.e., feature).\n",
+ "3. Compute the maximum of the row minimums.\n",
+ "4. Compute the average of the column maximus.\n",
+ "\n",
+ "**Hint:** The output for questions `4.3`, `4.4`, will be a *scalar*. To make sure you are aggregating over the correct values, check the shape of the intermediate resulting array first.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "26b18248",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Solution for 4.1\n",
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b51ed6f9",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Solution for 4.2\n",
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "752f51ff",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Solution for 4.3\n",
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bdfb54b6",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Solution for 4.4\n",
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "deaae13d",
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
+ "source": [
+ "### 5. Array Transpose\n",
+ "When transposing a `2x3` array, we get a `3x2` array. Matrix transpose has a lot of significance in linear algebra and you will rely on it many times in your assignments.\n",
+ "\n",
+ "Using the transpose of the original `attributes` array:\n",
+ "\n",
+ "1. Compute the same statistics of the question 4.2.\n",
+ "1. Compute the same statistics of the question 4.3.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0b3d09ae",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Solution for 5.1\n",
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ff9dbd08",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Solution for 5.2\n",
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9b2df45d",
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
+ "source": [
+ "### 6. Sorting and Indexing\n",
+ "Sort the `names` array alphabetically, and then apply the same ordering to the **columns** of the `attributes` array, in order to preserve the correspondence between them.\n",
+ "\n",
+ "Hint: be careful when applying the sorting of `names` to `attributes` and think about the role of each axis.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9536b1ad",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1f0dc8c2",
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
+ "source": [
+ "### 7. Data Standardization\n",
+ "Standardization (not to be confused with normalization), is a preprocessing step that is commonly used in many machine learning models and ensures that all features are normally distributed (i.e., they have zero mean and unit variance).\n",
+ "\n",
+ "To do this, you need to transform the data as follows: \n",
+ "1. Remove the mean value of each feature (i.e., centering).\n",
+ "2. Divide the features by their standard deviation (i.e., rescaling).\n",
+ "\n",
+ "Save the standardized version of the `attributes` array to `attributes_norm`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1d9ea13d",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f86fc083",
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
+ "source": [
+ "### 8. Matrix Operations\n",
+ "We are going to work with following slices of the `attributes` array (see the cell below). You will compute some simple operations without using NumPy's builtin methods, but you may use them to check that your solution is correct."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3ec7f5b7-662e-4bc8-a154-5bd93ea2a518",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "slice1 = attributes[6:10]\n",
+ "slice2 = attributes[76:80]\n",
+ "\n",
+ "print_array(slice1)\n",
+ "print_array(slice2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2d120955",
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ },
+ "tags": []
+ },
+ "source": [
+ "**1\\.** Compute the dot product between each vector (i.e., row) of `slice1`, with the corresponding vector of `slice2`. This means, the 1st vector `slice1` with the 1st vector of `slice2`, the 2nd vector `slice1` with the 2nd vector of `slice2` etc. Use numpy, but avoid using `np.dot` or for loops. Think about the definition of the dot product.\n",
+ "\n",
+ "The expected output is `[514410.1698, 661579.8319, 797379.7166, 494338.7313]`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "28dc7dae-854f-4701-bc42-7d10524e7d9d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2b4ba17d",
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
+ "source": [
+ "**2\\.** Compute the cosine similarity between 7th and 77th rows (use 0-based indexing) of the `attributes` array using the dot product. \n",
+ "\n",
+ "(0-based indexing, means you should use the vectors `attributes[7]` and `attributes[77]` )\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c7db29f9",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1d6d022c",
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
+ "source": [
+ "Cosine similarity is used a lot in machine learning and has many nice properties compared to other metrics, such as a cosine. One of them is that its values are in the `[-1, 1]` range regardless of the properties of the vector space (e.g., dimensionality).\n",
+ "\n",
+ "**3\\.** Compute the cosine similarity between the 7th and 77th rows (use 0-based indexing) of the `attributes_norm` array. How can you explain the difference in their cosine similarities? Why did these vectors look very similar before standardization, but dissimilar afterwards?\n",
+ "\n",
+ "You can use the function below:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eb924b7c",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "from scipy import spatial\n",
+ "cos_similarity = lambda x, y: 1 - spatial.distance.cosine(x, y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1120478e",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# write your code here"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/2025/labs/lab1/wine.csv b/2025/labs/lab1/wine.csv
new file mode 100644
index 0000000000000000000000000000000000000000..10e3f2cdb6c241c9d899d64adbcdf736ceef2193
--- /dev/null
+++ b/2025/labs/lab1/wine.csv
@@ -0,0 +1,179 @@
+Wine,Alcohol,Malic.acid,Ash,Acl,Mg,Phenols,Flavanoids,Nonflavanoid.phenols,Proanth,Color.int,Hue,OD,Proline
+1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065
+1,13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050
+1,13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185
+1,14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480
+1,13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735
+1,14.2,1.76,2.45,15.2,112,3.27,3.39,.34,1.97,6.75,1.05,2.85,1450
+1,14.39,1.87,2.45,14.6,96,2.5,2.52,.3,1.98,5.25,1.02,3.58,1290
+1,14.06,2.15,2.61,17.6,121,2.6,2.51,.31,1.25,5.05,1.06,3.58,1295
+1,14.83,1.64,2.17,14,97,2.8,2.98,.29,1.98,5.2,1.08,2.85,1045
+1,13.86,1.35,2.27,16,98,2.98,3.15,.22,1.85,7.22,1.01,3.55,1045
+1,14.1,2.16,2.3,18,105,2.95,3.32,.22,2.38,5.75,1.25,3.17,1510
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