Week 1

Course context and overview: mathematization of humanities knowledge. Introduction to main DH tools. Logic, formal languages, code, and associated notation.

Logbook review due Friday.

Week 2

Statistics concepts and methods. Applications to data structures. Data processing in DH.

Assignment 1 and logbook review due Friday.

Week 3

Fundamentals of combinatorics and mathematical probability. Predictive text tools.

Logbook review due Friday.

Week 4

Statistics and probability, continued. Conditional probability. Applications to textual analysis and data mining.

Assignment 2 and logbook review due Friday.

Week 5

Quest review. Introduction to graph theory.

Quest 1 on Wednesday; logbook review due Friday.

Week 6

Graph theory, continued. Applications to network analysis.

Assignment 3 and logbook review due Friday.

Week 7

Linear algebra fundamentals: vectors and weighted sums. Introducing machine learning concepts of large language models and generative pre-trained transformers.

None (Steinmetz Symposium Friday, no class).

Week 8

Quest review. Matrices, matrix manipulation and model training.

Quest 2 on Wednesday; logbook review due Friday.

Week 9

Vector representation and word prediction in large language models: “stochastic parrots”?

Assignment 4 and logbook review due Friday (depending on enrollment, Friday may be used for final project presentations).

Week 10

Final project presentations.

Final project presentations Monday, Wednesday, Friday; final project reports due by end of final exam period.