Description
Assignment 1
This assignment will consist of Three parts (A, B and C)
* Part A will be about getting acquainted with all tools / hardware / environments and deploying your first learning algorithms on relative simple datasets
* Part B will be about analyzing and deploying machine learning on a more complex dataset
* Part C will be about analyzing and deploying machine learning on your own generated dataset
Before you can start with the first assignment you should first setup your PC, install the required software and follow some tutorials.
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## Preparation
You have to <ins>**make groups of 3 students**</ins> to work on the assignments.
– You can ***assign yourself to a group in Canvas***.
– Groups can be made across classes,
– groups of 2 students or less (without consent of the teacher) will be reassigned!
### Software Environment
Make sure you have setup the [Software environment](../Setting_up_Software.md) needed for this assignment.
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– Make sure you assembled the hardware and installed the required software and connect to your Jetson –>
### Machine Learning Tutorials
We provide some tutorials how to get started with Machine Learning / use specific packages / import datasets / analyze data / display results / implement and compare ML algorithms.
The knowledge learned from these tutorials will be required for making the Assignments.
Follow [These Tutorials](Tutorials/README.md) first before you continue!
### Hints
Additional info can be found in the [tips and trick document](Tips_and_Tricks.md)
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## The Assignments
| Before you start the assignment make sure you followed the above tutorials. |
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### Assignment 1 – Part A
In Part A you will set-up the system and executed your first learning algorithm.
You can find a jupyter notebook here.
The notebook contains the assignment description!
> [Assignment1_partA.md](Assignment1_partA.md)
* Add your group number, names and code in the given notebooks
* Answer all questions that are located in the notebooks
* place your comments / conclusion / insights on all executed steps
### Assignment 1 – Part B
In part B and Part C you will analyze real data and generate a dataset for your own learning problem.
In this part you will analyze the data of a given dataset and train machine learning models on this data.
> [Assignment1_partB.md](Assignment1_partB.md)
* Add your group number, names and code in the given notebooks
* Answer all questions that are located in the notebooks
* place your comments / conclusion / insights on all executed steps
### Assignment 1 – Part C
In part B and Part C you will analyze real data and generate a dataset for your own learning problem.
In this part you will generate your own dataset and you will have to train your models on that data.
> [Assignment1_partC.md](Assignment1_partC.md)
* Add your group number, names and code in the given notebooks
* Answer all questions that are located in the notebooks
* place your comments / conclusion / insights on all executed steps
* make a short video (max 3 minutes) explaining & showing the process from dataset creation to final result
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## Deliverables Assignment 1
At the end you have to hand in all the notebook of Assignment 1. When you hand it in you should make sure that you saved it with all output visible (after execution), so we can evaluate your notebooks output without directly running it. In addition (to be sure) you should also save a pdf of the final result.
For assignment C we also want a short video which shows all the design steps (from collecting data till deployment), in this video you should <ins>show and explain</ins> what you did, which design choices you made and why!
***Final deliverables Assignment 1***:
* Notebooks Part A, Part B, Part C (saved after execution, so all outputs are visible).
* A pdf of all these notebooks in the same state (as backup).
* The data(sets) that you generated for Part C, so we can run your code if needed.
* A video (max 3 minutes) explaining & showing the process from dataset creation to final result of Part C.
* The Assignment per group has to be <ins>**uploaded to Canvas before the given due date!**</ins>
| NOTE: Make sure you answer all questions that are given in all the notebooks and that you draw some conclusions/insights per execution step. Just showing the output is not sufficient. |
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