Notes:
(a) Submit a zip file of the completed iPython notebooks to Tabula. Make sure to put comments in your
code explaining what you have done and what you have observed from the results.
(b) This assignment will contribute to 25% of your overall mark.
This assignment consists of 7 exercises which are split into 4 parts. The first 3 parts can be found in the lab
of Classification while the last part can be found in the lab of Dimensionality Reduction. Please download
the corresponding ipynb files from the module web page
https://warwick.ac.uk/fac/sci/dcs/teaching/material/cs909.
Part 1: Binary Classification of MNIST Dataset
In the first set of tasks, you will evaluate a number of popular classifiers for the task of recognising
handwritten digits from MNIST dataset. Specifically, we will focus on distinguishing between 7 and
9 which are known to be a hard pair. You will use the scikit-learn libraries.
Binary Classification in scikit-learn
All classifiers in scikit-learn follow a common pattern that makes life much easier. Follow these steps for all
the tasks below.
1. Instantiate the classifier with appropriate parameters
2. Train/fit the classifier with training data and correct labels
3. Test the classifier with unseen data
4. Evaluate the performance of classifier