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doc/LectureNotes/.ipynb_checkpoints/E1-checkpoint.ipynb

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doc/LectureNotes/E1.ipynb

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"source": [
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"Welcome to FYS-STK3155/4155!\n",
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"\n",
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"In this first week will focus on getting you set up with the programs you are going to be using throughout this course. We expect that many of you will encounter some trouble with setting these programs up, as they can be extremely finnicky and prone to not working the same on all machines, so we strongly encourage you to not get discouraged, and to show up to the group-sessions where we can help you along. The group sessions are also the best place to find group partners for the projects and to be challenged on your understanding of the material, which are both essential to doing well in this course.\n",
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"In this first week will focus on getting you set up with the programs you are going to be using throughout this course. We expect that many of you will encounter some trouble with setting these programs up, as they can be extremely finnicky and prone to not working the same on all machines, so we strongly encourage you to not get discouraged, and to show up to the group-sessions where we can help you along. The group sessions are also the best place to find group partners for the projects and to be challenged on your understanding of the material, which are both essential to doing well in this course. We strongly encourage you to form groups of 2-3 participants. \n",
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"\n",
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"If you are unable to complete this weeks exercises, don't worry, this will likely be the most frustrating week for many of you. You have time to get back on track next week, especially if you come to the group-sessions!"
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"If you are unable to complete this weekss exercises, don't worry, this will likely be the most frustrating week for many of you. You have time to get back on track next week, especially if you come to the group-sessions! Note also that this week's set of exercises does not count for the additional score. The deadline for the weekly exercises is set to Fridays, at midnight."
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"id": "b5a4818a",
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"metadata": {},
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"Following the theme of the previous exercises, another way of improving the reproducibility of your results and shareability of your code is having a good handle on which python packages you are using.\n",
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"Following the themes from the previous exercises, another way of improving the reproducibility of your results and shareability of your code is having a good handle on which python packages you are using.\n",
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"\n",
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"There are many ways to manage your packages in Python, and you are free to use any approach you want, but in this course we encourage you to use something called a virtual environment. A virtual environemnt is a folder in your project which contains a Python runtime executable as well as all the packages you are using in the current project. In this way, each of your projects has its required set of packages installed in the same folder, so that if anything goes wrong while managing your packages it only affects the one project, and if multiple projects require different versions of the same package, you don't need to worry about messing up old projects. Also, it's easy to just delete the folder and start over if anything goes wrong.\n",
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"\n",
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"Virtual environments are typically created, activated, managed and updated using terminal commands, but for now we recommend that you let VS Code handle it for you to make the coding experience much easier. If you are familiar with another approach for virtual environments that works for you, feel free to keep doing it that way.\n"
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"Virtual environments are typically created, activated, managed and updated using terminal commands, but for now we recommend that you let for example VS Code (a popular cross-paltform package) handle it for you to make the coding experience much easier. If you are familiar with another approach for virtual environments that works for you, feel free to keep doing it that way.\n"
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"id": "d19ebd67",
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"Great job on getting through all of that! Now it's time to do some actual machine learning!\n",
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"Great job on getting through all of that! Now it is time to do some actual machine learning!\n",
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"\n",
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"**a)** Complete the code below so that you fit a second order polynomial to the data. You will need to look up some skelarn documentation online (look at the imported functions for hints).\n",
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"**a)** Complete the code below so that you fit a second order polynomial to the data. You will need to look up some scikit-learn documentation online (look at the imported functions for hints).\n",
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"\n",
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"**b)** Compute the mean square error for the line model and for the second degree polynomial model."
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.0"
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"version": "3.9.15"
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