Incorporate Ben's feedback (#6)

* Fix setup

* Add feedback

* Start to address todos

* Additional changes and add readme outline

* Test on aws machine

---------

Co-authored-by: Benjamin Gallusser <[email protected]>

README.md

@@ -6,7 +6,7 @@ Make sure that you are inside of the `intro_ml` folder by using the `cd` command
 
 Run the setup script to create the environment for this exercise and download the dataset.
 ```bash
-sh setup.sh
+source setup.sh
 ```
 
 Launch a jupyter environment
@@ -15,4 +15,31 @@ Launch a jupyter environment
 jupyter lab
 ```
 
-...and continue with the instructions in the notebook.
+...and continue with the instructions in the notebook `exercise.ipynb`.
+
+## Exercise
+
+### Part A: The Linear Classifier
+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)
+
+### Part B: Random Forest Classifier
+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)
+
+### Part C: Feature Engineering
+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)