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Introductory Applied Machine Learning (IAML) Coursework 1 - Semester 2, January 2022.

This coursework does not count towards your grade, but it is excellent practice for you in process, coding and explanation, for Courswork 2, which counts for 30% of your mark. We strongly encourage you to work on it and submit it as if it did count towards your mark. It will not be marked individually but we will give some class-wide feedback.

Author: Nigel Goddard

Important Instructions

It is important that you follow the instructions below carefully for things to work properly.

You need to set up and activate your environment as you would do for your labs, see Learn section on Labs. In brief:

  1. Log into the Noteable and start or reconnect to a Standard Notebook.
  2. Cick on the "+GitRepo" button, then copy this link, https://github.com/uoe-iaml/DL-S2-2022-CW1, to the "Git Repository URL" box in the dialog.
  3. Replace "master" with "main" in the "branch" box in the dialog.
  4. Click on "clone" to import all the lab materials to your Noteable space.
  5. Click on IAML-21-22-S2-DL-CW1.ipynb to start up the assignment Notebook.

You will need to use Noteable to create one of the files you will submit (the PDF). Do NOT create the PDF in some other way. If you want to develop your answers in your own environment, you should make sure you are using the same packages we are using, by running the cell which does imports below.

Read the instructions in this notebook carefully, especially where asked to name variables with a specific name. Wherever you are required to produce code you should use code cells, otherwise you should use markdown cells to report results and explain answers. In most cases we indicate the nature of answer we are expecting (code/text), and also provide the required code/markdown cell.

The .csv files that you will be using are located in the ./datasets directory that is included in the git repository with this file.

Keep your answers brief and concise. Most written questions can be answered in 2-3 lines, a few will take longer.

Make sure to distinguish between attributes (columns of the data) and features (which typically refers only to the independent variables, i.e. excluding the target variables).

Make sure to show all your code/working.

Write readable code. While we do not expect you to follow PEP8 to the letter, the code should be adequately understandable, with plots/visualisations correctly labelled. Do use inline comments when doing something non-standard. When asked to present numerical values, make sure to represent real numbers in the appropriate precision to exemplify your answer.

You will see \pagebreak at the start of each subquestion. Do not remove these.

SUBMISSION Process

This assignment will not count towards your course mark. We ask you to submit answers to all questions.

You will submit a PDF of your Notebook, and the Notebook itself. Your grade will be based on the PDF, we will only use the Notebook if we need to see details. You must use the following procedure to create the materials to submit and then submit them.

  1. Make sure your Notebook and the datasets are in Noteable and will run. If you developed your answers in Noteable, this is already done. If you developed your answers in your own environment, you will need to uploading your Notebook to Noteable and make sure it runs ok.

  2. Select Kernel->Restart & Run All to create a clean copy of your submission, this will run the cells in order from top to bottom. This may take a while (minutes) to complete, ensure that all the output and plots have completed before you proceed by waiting for the last cell's banner message to be printed.

  3. Select File->Download as->PDF via LaTeX (.pdf) and wait for the PDF to be created and downloaded.

  4. Select File->Download as->Notebook (.ipynb)

  5. You now should have in your download folder the pdf and the notebook. Rename them sNNNNNNN.pdf and sNNNNNNN.ipynb, where sNNNNNNN is your matriculation number (student number), e.g. s1234567.

  6. Now submit the PDF to Gradescope on Learn. There is video guidance on Learn (Assessment->Assignment Submission) on how to do this. It is very important that during Gradescope submission you indicate which pages of your PDF correspod to which of the questions - the video gives guidance on this, you can tick multiple pages for each question.

  7. Finally submit the Notebook itself (named as indicated in 5 above) to Learn. You do this at Assessment->Assignment Submission->Assignment 1 - Submit your Notebook

The submission deadline for this assignment is 18th February 2021 at 16:00 UK time (UTC). Don't leave it to the last minute!

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