diff --git a/assignment 5/README.md b/assignment 5/README.md
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+# Description of Assignment 4
+### Author: [Fotios Lygerakis](https://github.com/ligerfotis)
+
+### Implement the Perceptron algorithm for classification of the Iris dataset.
+* Use load_iris() from sklearn.datasets to load the Iris dataset.
+    * The Iris dataset is a multiclass classification dataset with 3 classes.
+    * The Iris dataset has 4 features and 150 samples.
+    * The Iris dataset is a balanced dataset with 50 samples per class.
+* Split the dataset into train and test sets.
+* Scale the features.
+* Implement the Perceptron algorithm for classification.
+    * Use the unit step function as the activation function.
+* Evaluate the model on the test set.
+    * Use accuracy_score() from sklearn.metrics to calculate the accuracy of the model.
+* Do some hyperparameter tuning to improve the accuracy of the model.
+    * Try different values of the learning rate and the number of iterations.
+    * Print the accuracy of the model for different values of the learning rate and the number of iterations.
+### Implement Non-linear feature transformation for regression of the Concrete Compressive Strength dataset.
+* Use read_excel() from pandas to load the dataset.
+* The Concrete Compressive Strength dataset
+    * is a regression dataset with 1 target variable.
+    * has 8 features and 1030 samples.
+    * has 1 target variable.
+* Split the dataset into train and test sets.
+* Scale the features.
+* **Polynomial feature transformation**
+    * Implement the polynomial_features() function to transform the features.
+    * Use LinearRegression() from sklearn.linear_model to train a linear regression model.
+    * Evaluate the model on the test set.
+        * Use mean_squared_error() from sklearn.metrics to calculate the mean squared error of the model.
+        * Use r2_score() from sklearn.metrics to calculate the R2 score of the model.
+    * Train a linear regression model on the polynomial features varying the degree of the polynomial from 1 to 4.
+    * Evaluate the models trained on the polynomial features on the test set and compare the mean squared error of the models.
+    * Discuss the results in the report.
+* **Radial Basis Function (RBF) feature transformation**
+    * Implement the rbf_features() function to transform the features.
+    * Use LinearRegression() from sklearn.linear_model to train a linear regression model.
+    * Evaluate the model on the test set.
+        * Use mean_squared_error() from sklearn.metrics to calculate the mean squared error of the model.
+        * Use r2_score() from sklearn.metrics to calculate the R2 score of the model.
+    * Train a linear regression model on the RBF features varying the gamma parameter from 0.1 to 10.
+    * Evaluate the models trained on the RBF features on the test set and compare the mean squared error of the models.
+    * Discuss the results in the report.
+### **(Bonus)** Implement the Multilayer Perceptron algorithm (2 layers) for regression of the Concrete Compressive Strength dataset.
+* Use train_test_split() from sklearn.model_selection to split the dataset into train and test sets.
+* Use StandardScaler() from sklearn.preprocessing to scale the features.
+* Implement the Multilayer Perceptron algorithm for regression.
+    * The Multilayer Perceptron algorithm is a neural network with 2 layers.
+    * Implement the forward propagation algorithm.
+    * Implement the backward propagation algorithm.
+    * Use the tanh function as the activation function for the hidden layer.
+    * Use the identity function as the activation function for the output layer.
+* Evaluate the model on the test set.
+* Do some hyperparameter tuning to improve the accuracy of the model.
+    * Try different values of the learning rate and the number of epochs.
+    * Plot the mean squared error of the model for different values of the learning rate and the number of epochs.
+* Compare the performance of the Multilayer Perceptron algorithm with the Linear Regression algorithm.
+    * Use LinearRegression() from sklearn.linear_model to train a linear regression model.
+    * Use mean_squared_error() from sklearn.metrics to calculate the mean squared error of the linear regression model.
+* Compare the mean squared error of the linear regression model with the mean squared error of the multilayer perceptron model.
diff --git a/assignment 5/iml_assignmnet5_unsolved.ipynb b/assignment 5/iml_assignmnet5_unsolved.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Perceptron Algorithm for Classification of Iris Dataset"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(150, 4)\n",
+      "(150,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load the iris dataset\n",
+    "from sklearn.datasets import load_iris\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "import numpy as np\n",
+    "\n",
+    "iris = load_iris()\n",
+    "X = iris.data\n",
+    "y = iris.target"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Preprocess the data"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "outputs": [],
+   "source": [
+    "# Preprocess the data\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# ToDo: split the data into train and test sets\n",
+    "X_train, X_test, y_train, y_test = None, None, None, None"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Define the perceptron algorithm"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "outputs": [],
+   "source": [
+    "# Define the perceptron algorithm\n",
+    "class Perceptron:\n",
+    "    def __init__(self, learning_rate=0.01, n_iters=1000):\n",
+    "        pass\n",
+    "\n",
+    "    # define the fit function to train the model\n",
+    "\n",
+    "    # define the predict function to predict labels\n",
+    "\n",
+    "    def _unit_step_func(self, x):\n",
+    "        pass"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Train the model"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "p = Perceptron(learning_rate=0.01, n_iters=1000)\n",
+    "p.fit(X_train, y_train)\n",
+    "predictions_train = p.predict(X_train)\n",
+    "predictions = p.predict(X_test)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Evaluate the model"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Perceptron classification train accuracy 0.3416666666666667\n",
+      "Perceptron classification accuracy 0.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "# evaluate train accuracy\n",
+    "print(\"Perceptron classification train accuracy\", accuracy_score(y_train, predictions_train))\n",
+    "print(\"Perceptron classification accuracy\", accuracy_score(y_test, predictions))"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Non-linear feature transformation on the concrete compressive strength dataset"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "outputs": [],
+   "source": [
+    "from itertools import combinations_with_replacement\n",
+    "\n",
+    "# ToDO: implement the polynomial_features() function\n",
+    "def polynomial_features(X, degree):\n",
+    "    pass"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "outputs": [],
+   "source": [
+    "# Non-linear feature transformation\n",
+    "import pandas as pd\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "\n",
+    "# load the concrete compressive strength dataset\n",
+    "df = pd.read_excel('Concrete_Data.xls')\n",
+    "\n",
+    "# ToDo: split the data into train and test sets\n",
+    "X_train, X_test, y_train, y_test = None, None, None, None\n",
+    "\n",
+    "# transform the features into second degree polynomial features\n",
+    "X_train_poly_custom = polynomial_features(X_train.values, degree=2)\n",
+    "X_test_poly_custom = polynomial_features(X_test.values, degree=2)\n"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Train the linear regression model"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean squared error (train poly custom): 64.55\n",
+      "Mean squared error (test poly custom): 58.28\n",
+      "Mean squared error (train): 110.66\n",
+      "Mean squared error (test): 95.98\n",
+      "R^2 (train poly custom): 0.77\n",
+      "R^2 (test poly custom): 0.77\n",
+      "R^2 (train): 0.61\n",
+      "R^2 (test): 0.63\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "lr_poly_custom = LinearRegression()\n",
+    "lr = LinearRegression()\n",
+    "# fit the model\n",
+    "lr_poly_custom.fit(X_train_poly_custom, y_train)\n",
+    "lr.fit(X_train, y_train)\n",
+    "# predict values from the polynomial transformed features\n",
+    "predictions_poly_custom_train = lr_poly_custom.predict(X_train_poly_custom)\n",
+    "predictions_poly_custom = lr_poly_custom.predict(X_test_poly_custom)\n",
+    "# predict values from the original features\n",
+    "predictions_train = lr.predict(X_train)\n",
+    "predictions = lr.predict(X_test)\n",
+    "\n",
+    "# mean squared error\n",
+    "print(\"Mean squared error (train poly custom): {:.2f}\".format(mean_squared_error(y_train, predictions_poly_custom_train)))\n",
+    "print(\"Mean squared error (test poly custom): {:.2f}\".format(mean_squared_error(y_test, predictions_poly_custom)))\n",
+    "print(\"Mean squared error (train): {:.2f}\".format(mean_squared_error(y_train, predictions_train)))\n",
+    "print(\"Mean squared error (test): {:.2f}\".format(mean_squared_error(y_test, predictions)))\n",
+    "\n",
+    "# coefficient of determination (R^2)\n",
+    "print(\"R^2 (train poly custom): {:.2f}\".format(r2_score(y_train, predictions_poly_custom_train)))\n",
+    "print(\"R^2 (test poly custom): {:.2f}\".format(r2_score(y_test, predictions_poly_custom)))\n",
+    "print(\"R^2 (train): {:.2f}\".format(r2_score(y_train, predictions_train)))\n",
+    "print(\"R^2 (test): {:.2f}\".format(r2_score(y_test, predictions)))\n",
+    "\n"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "RBFs on the California Housing Prices dataset"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "outputs": [],
+   "source": [
+    "# ToDO: implement the rbf_kernel() function\n",
+    "def rbf_kernel(X, centers, gamma):\n",
+    "    pass"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Linear regression on original data:\n",
+      "MSE: 0.5558915986952443\n",
+      "R^2: 0.5757877060324508\n",
+      "\n",
+      "Linear regression on RBF-transformed data:\n",
+      "MSE: 0.37106446913117447\n",
+      "R^2: 0.7168330839511696\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import fetch_california_housing\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "\n",
+    "# Load the California Housing Prices dataset\n",
+    "data = fetch_california_housing()\n",
+    "X = data['data']\n",
+    "y = data['target']\n",
+    "\n",
+    "# ToDo: split the data into training and testing sets\n",
+    "\n",
+    "# ToDo: standardize the data\n",
+    "X_train_std = None\n",
+    "X_test_std = None\n",
+    "\n",
+    "# Choose the number of centroids and the RBF kernel width\n",
+    "num_centroids = 100\n",
+    "gamma = 0.1\n",
+    "\n",
+    "# Randomly select the centroids from the training set\n",
+    "np.random.seed(42)\n",
+    "idx = np.random.choice(X_train_std.shape[0], num_centroids, replace=False)\n",
+    "centroids = X_train_std[idx]\n",
+    "\n",
+    "# Compute the RBF features for the training and testing sets\n",
+    "rbf_train = rbf_kernel(X_train_std, centroids, gamma)\n",
+    "rbf_test = rbf_kernel(X_test_std, centroids, gamma)\n",
+    "\n",
+    "# Fit a linear regression model on the original and RBF-transformed data\n",
+    "linreg_orig = LinearRegression().fit(X_train_std, y_train)\n",
+    "linreg_rbf = LinearRegression().fit(rbf_train, y_train)\n",
+    "\n",
+    "# Evaluate the models on the testing set\n",
+    "y_pred_orig = linreg_orig.predict(X_test_std)\n",
+    "mse_orig = mean_squared_error(y_test, y_pred_orig)\n",
+    "r2_orig = r2_score(y_test, y_pred_orig)\n",
+    "\n",
+    "y_pred_rbf = linreg_rbf.predict(rbf_test)\n",
+    "mse_rbf = mean_squared_error(y_test, y_pred_rbf)\n",
+    "r2_rbf = r2_score(y_test, y_pred_rbf)\n",
+    "\n",
+    "# Print the results\n",
+    "print(\"Linear regression on original data:\")\n",
+    "print(\"MSE:\", mse_orig)\n",
+    "print(\"R^2:\", r2_orig)\n",
+    "\n",
+    "print(\"\\nLinear regression on RBF-transformed data:\")\n",
+    "print(\"MSE:\", mse_rbf)\n",
+    "print(\"R^2:\", r2_rbf)\n"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# **(Bonus)** Multilayer Perceptron Algorithm for Regression of Concrete Compressive Strength Dataset"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Download the Concrete Compressive Strength Dataset from the UCI Machine Learning Repository."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1030, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Download the Concrete Compressive Strength Dataset from the UCI Machine Learning Repository.\n",
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "# ToDo: load the dataset"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Preprocess the data"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "outputs": [],
+   "source": [
+    "# Preprocess the data\n",
+    "\n",
+    "# ToDo: normalize the features\n",
+    "\n",
+    "# ToDo: split the data into training and testing sets"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Define the multilayer perceptron algorithm"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "outputs": [],
+   "source": [
+    "# ToDo: Implement the functions in the MLP class\n",
+    "class MLP:\n",
+    "    def __init__(self, input_dim, hidden_dim, output_dim, lr=0.01, epochs=1000):\n",
+    "        pass\n"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Train the model"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "outputs": [],
+   "source": [
+    "# Create an instance of the MLP class\n",
+    "mlp = MLP(input_dim=X_train.shape[1], hidden_dim=10, output_dim=1, lr=0.01, epochs=1000)\n",
+    "# Train the model\n",
+    "mlp.fit(X_train, y_train)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Evaluate the model"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Squared Error: 36.8911071801165\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate the model\n",
+    "y_pred = mlp.predict(X_test)\n",
+    "print(\"Mean Squared Error:\", mean_squared_error(y_test, y_pred))"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Compare the results with the linear regression model"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Squared Error: 95.97548435337708\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ToDo: fit a linear regression model on the training data"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}