diff --git a/Day3/Day3_InClassExercises/Day3_InClassExercises.ipynb b/Day3/Day3_InClassExercises/Day3_InClassExercises.ipynb
deleted file mode 100644
index ec97af9..0000000
--- a/Day3/Day3_InClassExercises/Day3_InClassExercises.ipynb
+++ /dev/null
@@ -1,609 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "2c0e7067",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "# MSEE UQ short course:  The $\\texttt{UQpy}$ library\n",
-    "\n",
-    "Application of Inference using the $\\texttt{UQpy}$ module $\\texttt{inference}$. \n",
-    "\n",
-    "Detailed instructions on how to use this module can be found in the $\\texttt{UQpy}$ documentation.\n",
-    "\n",
-    "https://uqpyproject.readthedocs.io/en/latest/inference/index.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e7d3814e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3e3186d2",
-   "metadata": {},
-   "source": [
-    "# Exercise 1 \n",
-    "\n",
-    "This exercise aims to introduce the user to the Markov Chain Monte Carlo sampling methods available in $\\texttt{UQpy}$, since they are a prerequisite for advanced Bayesian inference methods such as $\\texttt{BayesianParameterEstimation}$\n",
-    "\n",
-    "- Specifically, the $\\texttt{MetropolisHastings}$ algorithms will be discussed.\n",
-    "- Ways to sample the target function, as well as several diagnostic will be provided."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "32dffcdc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from UQpy.distributions import DistributionND\n",
-    "\n",
-    "\n",
-    "class Rosenbrock(DistributionND):\n",
-    "    def __init__(self, p=20.):\n",
-    "        super().__init__(p=p)\n",
-    "\n",
-    "    def pdf(self, x):\n",
-    "        return np.exp(-(100 * (x[:, 1] - x[:, 0] ** 2) ** 2 + (1 - x[:, 0]) ** 2) / self.parameters['p'])\n",
-    "\n",
-    "    def log_pdf(self, x):\n",
-    "        return -(100 * (x[:, 1] - x[:, 0] ** 2) ** 2 + (1 - x[:, 0]) ** 2) / self.parameters['p']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b51f5df",
-   "metadata": {},
-   "source": [
-    "### Step 1\n",
-    "\n",
-    "Import the necessary libraries:\n",
-    "\n",
-    "1. From the $\\texttt{UQpy.sampling}$ module.\n",
-    "- $\\texttt{MetroplisHastings}$\n",
-    "\n",
-    "2. From the $\\texttt{UQpy.distributions}$ module.\n",
-    "- $\\texttt{Normal}$\n",
-    "- $\\texttt{JointIndependent}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b5a9ae18",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7ad07ef7",
-   "metadata": {},
-   "source": [
-    "### Step 1.1\n",
-    "\n",
-    "The $\\texttt{log}\\_\\texttt{target}$ function that we want to sample using the $\\texttt{MetropolisHastings}$ samping method is defined below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dfa1f4d9",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b30c105a",
-   "metadata": {},
-   "source": [
-    "### Step 2\n",
-    "\n",
-    "Create an instance of the $\\texttt{MetropolisHastings}$ class:\n",
-    "\n",
-    "- The funtion $\\texttt{log}\\_\\texttt{target}$ provided serves as the $\\texttt{log}\\_\\texttt{pdf}\\_\\texttt{target}$\n",
-    "- Since our proposal distribution is two dimensional, the value of the $\\texttt{dimension}$ parameter is set to $2$.\n",
-    "- We generate four chain for the needs of the $\\texttt{MetropolisHastings}$ method and as a result the parameter $\\texttt{n}\\_\\texttt{chains}$ is set to $4$.\n",
-    "- Finally the $\\texttt{burn}\\_\\texttt{length}$ and $\\texttt{jump}$ parameters are set to $500$, $50$ respectively.\n",
-    "\n",
-    "To initiate the sample generation using the $\\texttt{MetropolisHastings}$ algorithm, the $\\texttt{nsamples}$ parameter must be provided, either in the initializer, or after the initialization in the $\\texttt{run}$ function of the class.\n",
-    "- The number of samples  is chosen here to be $500$. ($\\texttt{nsamples}=500$)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "259244c3",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "55b51bc4",
-   "metadata": {},
-   "source": [
-    "### Step 2.1\n",
-    "\n",
-    "The samples generated with the aid of the $\\texttt{MetropolisHastings}$ class, can be retrieved using the $\\texttt{samples}$ attribute.\n",
-    "\n",
-    "- After retrieving the samples from the $\\texttt{MetropolisHastings}$ algorithm, create a scatter plot of the generated samples."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "024a8fdd",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b201d0de",
-   "metadata": {},
-   "source": [
-    "### Step 3\n",
-    "\n",
-    "The acceptance rate of the chains can be accessed using the $\\texttt{acceptance}\\_\\texttt{rate}$ attribute of the $\\texttt{MCMC}$ algorithm.\n",
-    "\n",
-    "If this metric is too low, then the provided input parameters make the sampling inefficient, as new samples to advance the chain are rarely choosen."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f8ce23f1",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "48ccbbdc",
-   "metadata": {},
-   "source": [
-    "### Step 3.1\n",
-    "\n",
-    "Using the $\\texttt{MCMC}$ generated samples, the following graphic diagnostic can be generated.\n",
-    "\n",
-    "- The evolution of the sampled values.\n",
-    "- The convergence of the parameter towards its final value\n",
-    "- The correlation of between the samples generated for the respective parameter.\n",
-    "\n",
-    "Note that the samples retrieved from any of the sampling algorithms in $\\texttt{UQpy}$ are a 2 dimensional $\\texttt{numpy}$ array. The number of rows represents the number of samples, while the columns represent the number of parameters.\n",
-    "\n",
-    "To create each one of the plots, for each column $\\texttt{j}$ of the samples \n",
-    "\n",
-    "- Parameter Convergence: $\\texttt{plot(range(nsamples), np.cumsum(samples[:, j]/range(nsamples))}$\n",
-    "- Samples Correlation: $\\texttt{acorr(samples[:, j]-np.mean(acorr(samples[:, j]))}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c34ebe52",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba93bdc2",
-   "metadata": {},
-   "source": [
-    "# Exercise 2 \n",
-    "\n",
-    "Use **information-theoretic** inference to select between regression models. Consider the simple model:\n",
-    "\n",
-    "$$y=m(\\theta) + \\epsilon$$\n",
-    "\n",
-    "where $m(\\cdot)$ is our numerical model that runs with $\\texttt{RunModel}$ and $\\epsilon$ is  noise. We have 50 synthetic measurements of $y$ using one of the following model:\n",
-    "    \n",
-    "1. $m_0(\\theta)=\\theta_{0} x$ (linear)\n",
-    "2. $m_1(\\boldsymbol{\\theta})=\\theta_{0} x + \\theta_{1} x^{2}$ (quadratic)\n",
-    "3. $m_2(\\boldsymbol{\\theta})=\\theta_{0} x + \\theta_{1} x^{2} + \\theta_{2} x^{3}$ (cubic)\n",
-    "\n",
-    "where $\\theta_i$, $i=0,1, 2$ are the parameters of the model. We have also added some Gaussian noise ($\\sim \\mathcal{N}(0, 1)$) in the measurements which is considered to be homogeneous. You are given the file $\\texttt{'data_ex1a.txt'}$ which contains the data. Select a statistical model from the set of three candidate models, given the data, using the $AIC_c$ information-theoretic criterion. You are provided with the file $\\texttt{pfn.py}$ that contains the three models, written in $\\texttt{Python}$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3806b95b",
-   "metadata": {},
-   "source": [
-    "### Step 1:\n",
-    "\n",
-    "Read the data from the file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "76adc29b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e5630f89",
-   "metadata": {},
-   "source": [
-    "### Step 2:\n",
-    "\n",
-    "Import the necessary libraries:\n",
-    "\n",
-    "1. From the $\\texttt{UQpy.Inference}$ module.\n",
-    "- $\\texttt{ComputationalModel}$\n",
-    "- $\\texttt{InformationModelSelection}$\n",
-    "\n",
-    "2. From $\\texttt{UQpy.run}\\_\\texttt{model.RunModel}$ module import $\\texttt{RunModel}$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "36740ab1",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4354cfd4",
-   "metadata": {},
-   "source": [
-    "### Step 3:\n",
-    "\n",
-    "Create instances of the $\\texttt{RunModel}$ class for the three models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a496b8a6",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "566e5c33",
-   "metadata": {},
-   "source": [
-    "### Step 4:\n",
-    "\n",
-    "Create instances of the $\\texttt{InferenceModel}$ class for the three models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4f5f2951",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f637c246",
-   "metadata": {},
-   "source": [
-    "### Step 5\n",
-    "\n",
-    "Perform model selection. Create an instance of $\\texttt{InfoModelSelection}$ class, employs information-theoretic criteria for model selection, using the $AIC_c$ criterion. \n",
-    "\n",
-    "\n",
-    "For each model print its  probability to generate the measurements."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fd2e4e02",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4e0d47aa",
-   "metadata": {},
-   "source": [
-    "# Exercise 3 \n",
-    "\n",
-    "\n",
-    "Learning the parameters of the quadratic model. Consider we have identified the quadratic model\n",
-    "\n",
-    "$$m_2(\\boldsymbol{\\theta})=\\theta_{0} x + \\theta_{1} x^{2}$$\n",
-    "\n",
-    "as the model that represents the data. Using the measurements, we want to learn the parameters $\\theta_{0}$ and $\\theta_{1}$ of the quadratic model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cf751e25",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "data_ex1a = np.loadtxt('data_ex1a.txt')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8ba245c2",
-   "metadata": {},
-   "source": [
-    "### Step 1:\n",
-    "\n",
-    "Import the necessary libraries.\n",
-    "\n",
-    "1. From the $\\texttt{UQpy.inference}$ module import:\n",
-    "- $\\texttt{BayesParameterEstimation}$\n",
-    "\n",
-    "2. From the $\\texttt{UQpy.distributions}$ module import:\n",
-    "- $\\texttt{Normal}$\n",
-    "- $\\texttt{JoinIndependent}$ \n",
-    "\n",
-    "3. From $\\texttt{UQpy.run}\\_\\texttt{RunModel}$ module import $\\texttt{RunModel}$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3e63e30e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "83cbce3b",
-   "metadata": {},
-   "source": [
-    "### Step 2:\n",
-    "\n",
-    "Create instances of the $\\texttt{RunModel}$ class for the quadratic model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "180e223f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0f753a22",
-   "metadata": {},
-   "source": [
-    "### Step 3:\n",
-    "\n",
-    "Create a joint distribution object for its parameters $\\boldsymbol{\\theta}$ that represents our prior belief. Each marginal distribution is Normally distributed but, we don't provide any information about its parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cb30500a",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b8376fbd",
-   "metadata": {},
-   "source": [
-    "### Step 4:\n",
-    "\n",
-    "Create an instance of the $\\texttt{ComputationalModel}$ class. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f6ca1c33",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d2e312b1",
-   "metadata": {},
-   "source": [
-    "### Step 5:\n",
-    "\n",
-    "Create a joint proposal distribution object for its parameters $\\boldsymbol{\\theta}$. Each marginal distribution is normally distributed, i.e., $\\theta_0 \\sim \\mathcal{N}(0.0, 0.1)$ and  $\\theta_1 \\sim \\mathcal{N}(0.0, 0.05)$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "76b25707",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5f189bde",
-   "metadata": {},
-   "source": [
-    "### Step 6:\n",
-    "\n",
-    "Create an instance of the $\\texttt{BayesParameterEstimation}$ class.  Select as sampling algorithm the $\\texttt{MetropolisHastings}$ algorithm of the $\\texttt{MCMC}$ class.\n",
-    "\n",
-    "Print the posterior values of the two parameters that are stored in the $\\texttt{samples}$ attribute of the $\\texttt{BayesParameterEstimation}$ object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "881474aa",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4eddbb88",
-   "metadata": {},
-   "source": [
-    "# Exercise 4 \n",
-    "\n",
-    "Use  **Bayesian model** selection to choose between regression models. Consider the problem of Exercise 1. In this case, each candidate model $m_i$ is given a prior probability $P(m_i) = 1/3$. For each model, our prior belief about its parameters $\\theta_i$ is that they are normally distributed as: \n",
-    "- $m_0(\\theta)$: $\\theta_0\\sim\\mathcal{N}(0,10)$.\n",
-    "- $m_1(\\boldsymbol{\\theta})$: $\\theta_0\\sim\\mathcal{N}(0,1)$, $\\theta_1\\sim\\mathcal{N}(0,1)$\n",
-    "- $m_2(\\boldsymbol{\\theta})$:  $\\theta_0\\sim\\mathcal{N}(0,1)$, $\\theta_1\\sim\\mathcal{N}(0,2)$, $\\theta_2\\sim\\mathcal{N}(0,0.25)$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "38c13a5b",
-   "metadata": {},
-   "source": [
-    "### Step 1:\n",
-    "\n",
-    "Import the necessary libraries\n",
-    "\n",
-    "1. From the $\\texttt{UQpy.inference}$ module import:\n",
-    "- $\\texttt{BayesModelSelection}$ \n",
-    "\n",
-    "2. From the $\\texttt{UQpy.distributions}$ module import:\n",
-    "- $\\texttt{Normal}$\n",
-    "- $\\texttt{JoinIndependent}$ \n",
-    "\n",
-    "3. From the file $\\texttt{data}\\_\\texttt{ex3.pkl}$ import the data to learn from"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "072a5c25",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d9c2d5bb",
-   "metadata": {},
-   "source": [
-    "### Step 2:\n",
-    "\n",
-    "Create instances of the $\\texttt{RunModel}$ class for the three models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c0a6b7e7",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e6a8e71",
-   "metadata": {},
-   "source": [
-    "### Step 3\n",
-    "\n",
-    "For each model $m_i(\\boldsymbol{\\theta})$ create a joint distribution object for its parameters $\\boldsymbol{\\theta}$ that represents our prior belief.\n",
-    "- $m_0(\\theta)$: $\\theta_0\\sim\\mathcal{N}(0,10)$.\n",
-    "- $m_1(\\boldsymbol{\\theta})$: $\\theta_0\\sim\\mathcal{N}(0,1)$, $\\theta_1\\sim\\mathcal{N}(0,1)$\n",
-    "- $m_2(\\boldsymbol{\\theta})$:  $\\theta_0\\sim\\mathcal{N}(0,1)$, $\\theta_1\\sim\\mathcal{N}(0,2)$, $\\theta_2\\sim\\mathcal{N}(0,0.25)$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "93c821bc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6924d7dd",
-   "metadata": {},
-   "source": [
-    "### Step 4\n",
-    "\n",
-    "Create instances of the $\\texttt{InferenceModel}$ class for the three models. In this case, for each model model you should also provide the $\\texttt{error}$\\_$\\texttt{covariance}$ as $\\texttt{np.ones(50)}$ (Why?)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0ba7baec",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a06c2a75",
-   "metadata": {},
-   "source": [
-    "### Step 5\n",
-    "\n",
-    "Create an instance of the $\\texttt{BayesModelSelection}$ class. To this end, you will need to utilize $\\texttt{MCMC}$ sampling methods:\n",
-    "\n",
-    "1. Import the necessary libraries. From the $\\texttt{UQpy.sampling}$ module import $\\texttt{MetropolisHastings}$ class. \n",
-    "2. For each model, create a joint proposal distribution for its parameters $\\boldsymbol{\\theta}$, where  each marginal distribution is normally distributed. \n",
-    "2. For each model, select to sample 2000 realizations of the parameters from their joint distribution.\n",
-    "\n",
-    "Print the posterior probability of each model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1e11d17d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "celltoolbar": "Slideshow",
-  "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": "3.9.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Day3/Day3_InClassExercises/HugoniotCalculations.py b/Day3/Day3_InClassExercises/HugoniotCalculations.py
deleted file mode 100644
index 960a827..0000000
--- a/Day3/Day3_InClassExercises/HugoniotCalculations.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import numpy as np
-
-
-def evaluate_model(samples):
-    a0 = samples[0, 0]
-    a1 = samples[0, 1]
-    a2 = samples[0, 2]
-    a3 = samples[0, 3]
-
-    particle_velocity = np.linspace(3.5, 10, num=50)
-    shock_velocity = [a0 + a1 * x + a2 * x ** 2 + a3 * x ** 3 for x in particle_velocity]
-
-    return shock_velocity
diff --git a/Day3/Day3_InClassExercises/__pycache__/pfn.cpython-39.pyc b/Day3/Day3_InClassExercises/__pycache__/pfn.cpython-39.pyc
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diff --git a/Day3/Day3_InClassExercises/__pycache__/pfn1.cpython-39.pyc b/Day3/Day3_InClassExercises/__pycache__/pfn1.cpython-39.pyc
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diff --git a/Day3/Day3_InClassExercises/__pycache__/pfn2.cpython-39.pyc b/Day3/Day3_InClassExercises/__pycache__/pfn2.cpython-39.pyc
deleted file mode 100644
index b0f00f1..0000000
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diff --git a/Day3/Day3_InClassExercises/activity_data.pkl b/Day3/Day3_InClassExercises/activity_data.pkl
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diff --git a/Day3/Day3_InClassExercises/activity_model.py b/Day3/Day3_InClassExercises/activity_model.py
deleted file mode 100644
index 29ae163..0000000
--- a/Day3/Day3_InClassExercises/activity_model.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import numpy as np
-
-
-def model_quadratic(theta):
-    domain = np.linspace(0, 10, 50)
-    inpt = np.array(theta).reshape((-1,))
-    return inpt[0] + inpt[1] * domain + inpt[2] * domain ** 2 + inpt[3] * domain ** 3
diff --git a/Day3/Day3_InClassExercises/data_ex1a.txt b/Day3/Day3_InClassExercises/data_ex1a.txt
deleted file mode 100644
index 4c75b24..0000000
--- a/Day3/Day3_InClassExercises/data_ex1a.txt
+++ /dev/null
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diff --git a/Day3/Day3_InClassExercises/data_ex1b.txt b/Day3/Day3_InClassExercises/data_ex1b.txt
deleted file mode 100644
index 2f2ac0b..0000000
--- a/Day3/Day3_InClassExercises/data_ex1b.txt
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diff --git a/Day3/Day3_InClassExercises/data_ex3.pkl b/Day3/Day3_InClassExercises/data_ex3.pkl
deleted file mode 100644
index de42a1a..0000000
Binary files a/Day3/Day3_InClassExercises/data_ex3.pkl and /dev/null differ
diff --git a/Day3/Day3_InClassExercises/pfn.py b/Day3/Day3_InClassExercises/pfn.py
deleted file mode 100644
index 388f739..0000000
--- a/Day3/Day3_InClassExercises/pfn.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import numpy as np
-
-domain = np.linspace(0, 10, 50)
-
-def model_linear(theta):
-    # this one takes one parameter vector theta and return one qoi
-    inpt = np.array(theta).reshape((-1,))
-    return inpt[0] * domain
diff --git a/Day3/Day3_InClassExercises/pfn1.py b/Day3/Day3_InClassExercises/pfn1.py
deleted file mode 100644
index 3809b3a..0000000
--- a/Day3/Day3_InClassExercises/pfn1.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import numpy as np
-
-domain = np.linspace(0, 10, 50)
-
-def model_quadratic(theta):
-    # this one takes one parameter vector theta and return one qoi
-    inpt = np.array(theta).reshape((-1,))
-    return inpt[0] * domain + inpt[1] * domain ** 2
\ No newline at end of file
diff --git a/Day3/Day3_InClassExercises/pfn2.py b/Day3/Day3_InClassExercises/pfn2.py
deleted file mode 100644
index 599c90b..0000000
--- a/Day3/Day3_InClassExercises/pfn2.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import numpy as np
-
-domain = np.linspace(0, 10, 50)
-
-def model_cubic(theta):
-    # this one takes one parameter vector theta and return one qoi
-    inpt = np.array(theta).reshape((-1,))
-    return inpt[0] * domain + inpt[1] * domain ** 2 + inpt[2] * domain ** 3
\ No newline at end of file