{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Organization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Lecturers\n",
    "\n",
    "|Sebastian Egli                    |                    Boris Thies|\n",
    "|----------------------------------|-------------------------------|\n",
    "|![](images/egli.jpg)              |          ![](images/thies.jpg)|\n",
    "|sebastian.egli@geo.uni-marburg.de | boris.thies@geo.uni-marburg.de|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Grading\n",
    "\n",
    "- **6 ECTS** credits are awarded for successfully completing this course.\n",
    "- Three case studies and three corresponding challenges build the basis of this course.\n",
    "- **Studienleistung**: \n",
    "    - The course credit consists of the submission of a well-prepared notebook for publication on the course website for one of the three **case studies** covered. \n",
    "    - Students are assigned to case studies at the beginning of the semester. \n",
    "    - In addition, at least one result must be submitted for each challenge before its respective deadline.\n",
    "- **Prüfungsleistung**: \n",
    "    - The course grade is determined by the evaluation of three well-prepared notebooks, one for each **challenge**.\n",
    "    - The deadline for the submission of these three notebooks will be communicated during the semester.\n",
    "    - Prediction results of the models must be reproducible, so please do always set a random state during model training! \n",
    "    - Evaluation criteria are:\n",
    "        - Selection of the ML model: Is the ML model suited to solve the problem stated in the challenge?\n",
    "        - Programming approach: Has a suitable programming approach been selected?\n",
    "        - Layout: Is the notebook clearly arranged? Does the layout follow a clear and understandable concept?\n",
    "        - Code quality: Have redundant code sections been removed from the notebook?\n",
    "        - Documentation: Has the code been fully and comprehensibly documented?\n",
    "        - Presentation of training/validation/testing procedures: Are the work packages adequately presented\n",
    "        - Visualization: Are intermediate steps (where useful for understanding) and model outputs clearly visualized? (title, axis labels, plot size, etc.)\n",
    "        - Use of Python libraries: Have the Python libraries from the course been used sensibly? (pandas, xarray, matplotlib, sklearn, tensorflow, …)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Software requirements & setup\n",
    "\n",
    "- Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html) on your system. Installation instructions can be found [here](https://conda.io/projects/conda/en/latest/user-guide/install/index.html).\n",
    "- Download this [environment.yml](https://gitlab.com/lcrsmarburg/teaching_material/-/snippets/2034239/raw/master/environment.yml?inline=false) file and use it to create a new conda environment: \n",
    "    ```SHELL\n",
    "    conda env create -f environment.yml\n",
    "    ```\n",
    "    This installs all packages in a conda environment called \"ML_course\", which we will be using\n",
    "    throughout this course.\n",
    "\n",
    "- Activate the environment via:\n",
    "    ```SHELL\n",
    "    conda activate ML_course\n",
    "    ```\n",
    "    Now all system variables should be set up correctly and we are good to go.\n",
    "\n",
    "- Most of the time, we will be working with JupyterLab. Start it via:\n",
    "    ```SHELL\n",
    "    jupyter lab\n",
    "    ```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Literature\n",
    "\n",
    "The following sources give interesting overviews and insights into the topics covered in this course:\n",
    "\n",
    "- Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152(March), 166–177. {cite}`Ma2019`\n",
    "\n",
    "- Maxwell, A. E., Warner, T. A., & Fang, F. (2018). Implementation of machine-learning classification in remote sensing: an applied review. International Journal of Remote Sensing, 39(9), 2784–2817. {cite}`Maxwell2018`"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}