Exercise 1: Introduction to Machine Learning

Setup

Make sure that you are inside of the intro_ml folder by using the cd command to change directories if needed.

Run the setup script to create the environment for this exercise and download the dataset.

source setup.sh

Launch a jupyter environment

jupyter lab

...and continue with the instructions in the notebook exercise.ipynb.

We will implement a basic linear classifier from scratch and train it to predict the cell cycle on a flow cytometry dataset.

You will learn

How to prepare a dataset for training, including
- Checking for class imbalance (Task 1.1)
- Correcting class imbalance (Task 1.2)
- Converting categorical data to one-hot encoding (Task 1.3)
The basic math behind a linear classifier
How to evaluate model performance (Tasks 2.1 - 2.3)

We will learn about Random Forest Classifiers and use scikit-learn to train one on our dataset.

You will learn

How to use scikit-learn's model objects
How to perform a hyperparameter search to optimize model performance (Tasks 3.1 - 4.1)

We will explore image filters and see if they can improve the performance of either our linear or random forest classifer.

You will learn

How to use scikit-image's filter modules (Task 5.1)
How to reuse what we have done so far to build a new dataset on filtered images and train your own model on the new dataset (Task 5.2)