> "What field of modern science relies heavily on "linear regression, bias and variance, cost function and gradient descent, regularisation and optimisation"?"

To dive into a particular topic:

> "Provide a course outline for a four week course, meeting twice a week, that focuses on linear regression in the context of machine learning and the relationship between inputs and outputs."

And to get to the actual material, zoom in some more:

> "Please expand Session 2: Simple Linear regression into an hour-long talk focused on Python coding approaches to the problem"

And again, to get some working code:

> "For topic #2, please provide an explicit code example of using numpy, pandas and scikit-learn to load a dataset, preprocess the data, and split it into training and testing sets"

Anyone can generate a course on any topic using this approach, with pretty good results.

Not saying they're not worth learning, but I think it's reasonable for them not to be included in the syllabus for that particular course.

Kind of like how learning memory management in C doesn't need to be a pre-requisite for a course on Python.

Note that learning about those things will likely make you a better LLM+NLP practitioner, in the same way that having a good grasp of memory management in C will help you be more effective at working with Python - but it's OK to leave them out of introductory courses.