use python notebooks to learn about kNN, the MNIST dataset, explore the curse of dimensionality, and try out the decision tree classifer in the sklearn library.

This needs to be written in Python. We are using Google Colab

This is a machine learning class.

Question is to set up data to implement K-nearest neighbours algorithms

[8pts total] Q1. k-Nearest Neighbours Classifier on Synthetic Data One of the important ways of both understanding and also debugging machine learning algorithms is to create your own, synthetic data sets. In this question you will implement a k-nearest neighbours (kNN) classifier and evaluate it on synthetic data. a) (2pts Create the data set. You will "train" a KNN classifier on the following training data: . The data is 2-dimensional points in a grid, such that the 11-coordinates and 19-coordinates both range from -1.5...1.5, with a spacing of 0.1 between points. A training point, I=(11,19), will be classified as follows: - Class 1 if |2||2