[devlovstad]

Slightly OT: I'm a master's student in computer science who focuses mostly on machine learning. Still, the best course I've ever taken was one on semantics and types, presenting many of the ideas in this article. Learning proof-writing using natural deduction with a ruthlessly rigorous teacher has made me much more precise when I write proofs in general, and learning about theory of computation and logic has given me a good mental model of how my programs execute.

While the course is an elective mostly focused on students interested in programming languages, I think all computer scientists can benefit from taking such a course. In a time where everyone wants to do AI, the course only had around 12 students out of a class of maybe 200 students.

Even more OT: Phil Wadler gave a talk at the programming language section of my university not too long ago, which I was much excited to see. Sadly, he chose a vague pop-sciency talk on AI which felt quite a bit outside his expertise.

[somethingsome]

Hey, do you have interesting slides/homeworks to share? I would be interested in taking a look

[devlovstad]

The course did not use slides but instead wrote everything on a whiteboard. The lecture notes are not public afaik.

The lecturer did suggest the following supplementary material:

- Michael Huth and Mark Ryan. Logic in Computer Science: Modelling and Reasoning about systems (2nd ed.). Cambridge University Press 2004. (Mainly chapters 1, 2)

- Glynn Winskel. The Formal Semantics of Programming Languages: An Introduction, MIT Press 1993. (Mainly chapters 1, 2, 3, 6, 11)

- Benjamin C. Pierce. Types and Programming Languages. MIT Press 2002. (Mainly chapters 5, 8, 9, 11, 12)