Update README following github pages site. (#32)

README.md

@@ -75,6 +75,59 @@ These are for recapping after the course in case you missed anything, and contai
 [black](https://black.readthedocs.io/en/stable/) code style.
 
 
+## Learning Objectives
+The key learning objective from this workshop could be simply summarised as:  
+_Provide the ability to develop ML models in PyTorch_.
+
+However, more specifically we aim to:
+
+* provide an understanding of the structure of a PyTorch model and ML pipeline,
+* introduce the different functionalities PyTorch might provide,
+* encourage good research software engineering (RSE) practice, and
+* exercise careful consideration and understanding of data used for training ML models.
+
+With regards to specific ML content we cover:
+
+* using ML for both classification and regression,
+* artificial neural networks (ANNs) and convolutional neural networks (CNNs)
+* treatment of both tabular and image data
+
+
+## Teaching Material
+
+### Slides
+The slides for this workshop can be viewed on the ICCS Summer School Website:
+  - [Teaching](https://cambridge-iccs.github.io/ml-training-material/slides.html)
+  - [Climate Applications](https://cambridge-iccs.github.io/ml-training-material/applications.html)
+
+The slides are generated from markdown using quarto.
+The raw markdown and html files can be found in the [slides](slides/) directory.
+
+### Exercises
+The exercises for the course can be found in the [exercises](exercises/) directory.  
+These take the form of partially complete jupyter notebooks.
+
+### Videos
+Videos from past workshops may be useful if you are following along independently.  
+These can be found on the [ICCS youtube channel](https://www.youtube.com/@instituteofcomputingforcli3982) 
+under the 2023 Summer School materials.
+
+### Worked Solutions
+Worked solutions for all of the exercises can be found in the [worked solutions](worked-solutions/) directory.  
+These are for recapping after the course in case you missed anything, and contain ideal solutions complete with
+[docstrings](https://peps.python.org/pep-0257/), outfitted with
+[type hints](https://docs.python.org/3/library/typing.html),
+[linted](https://docs.pylint.org/intro.html), and conforming to the
+[black](https://black.readthedocs.io/en/stable/) code style.
+
+If you were working on Colab you can open the worked solutions using the following links:
+
+* [Exercise 01](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/01_penguin_classification_solutions.ipynb)
+* [Exercise 02](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/02_penguin_regression_solutions.ipynb)
+* [Exercise 03](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/03_mnist_classification_solutions.ipynb)
+* [Exercise 04](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/04_ellipse_regression_solutions.ipynb)
+
+
 ## Preparation and prerequisites
 
 To get the most out of the session we assume a basic understanding in a few areas and 
@@ -211,7 +264,7 @@ jupyter notebook
 ```
 This command should then point you to the right location within your browser to use the notebook, typically [http://localhost:8888/](http://localhost:8888/).
 
-#### (Optional) Keep virtual environment persistent in Jupyter Notebooks
+#### (Optional) Keep virtual environment persistent in jupyter Notebooks
 
 The following step is sometimes useful if you're having trouble with your jupyter notebook finding the virtual environment. You will want to do this before
 launching the jupyter notebook.