changes to slides

[surbhigoel77]

Surbhi and I plan to spend more time explaining the theory of NNs with more focus on:

• Datasets. Dimensions.
• An expanded introduction:
  • Potential applications. What NNs can do...
  • Other alternatives to NNs.
  • More of an overview of pytorch. What is jax etc.
• Links to previous statistical techniques like regression.

Specific notes on current slides:

11/29

• neural is spelt nerual
• some weird aberration on the slide
• weird formatting here.

13/29

• "workshop lecture thing"

General notes on slides

• quite a brief overview of nns
  - do mention 3blue1brown ml course, can't do much better than this.
  - could we include some of this content in additional slides to better visualise some concepts?
• jumps straight into SGD without much intro
  • more of a justification for why we're using NNs
• more images
  • visualising data
    • tabulated data
  • potentials of what NNs can do
• comparison of different models for different datasets.
  • Other methods like SVMs instead of NNs.
• Drawbacks of NNs?
  - black-boxes. https://towardsdatascience.com/the-math-behind-kan-kolmogorov-arnold-networks-7c12a164ba95
• pytorch vs scikit learn
  • why we even need pytorch

Summary of what is currently covered

Penguin classification:

• loading penguin dataframe and inspecting data
• introduce a torch.utils.data.dataset.
• split into train and validation
• transforming the data using torchvision.transforms.Compose

Suggestions for guided tutorial / workshp

• Add some boilerplate / template for them to add to. Notes.
• Extensions. Compare different methods?

Other resources

• Stanford ML resources

Todos

• Think about what content we want to include as part of the expanded introduction.
  • Look into Quarto
  • How to improve slides, instead of starting with SGD.
  • Might be helpful to look at other resources here. https://github.com/phlippe/uvadlc_notebooks
• Notebook improvements.
  • Adding more content to the exercises to make them easier to complete
  • Statistical summary of data. max. / min / missing values ...
  • [ ]

[surbhigoel77]

Started with below slides:

1. What is Machine Learning
2. What are Neural Networks

[surbhigoel77]

@ma595 you can mention the slides that you change here

[ma595]

@jdenholm and I discussed this briefly last week. I'll attempt to summarise when I have some time. If @jdenholm can write a sentence summarising what he mentioned that would be great, but I appreciate that dedicating time to this might not be easy at the moment.

[jdenholm]

I think I suggested using PyTorch to fit a straight line, and then more general linear regression, as an intro to gradient descent and how it's useful for fitting functions.