Course term: Trinity
Course lecture information: 16 lectures
Course overview:

Category Theory has become an increasingly popular topic of study mainly due to its unmatched ability to organise and layer abstractions of all sorts and to find commonalities between seemingly unrelated structures. Aside from its appeal to the mathematical community, it has also branched out into science, informatics and industry. This course aims to give a taste of a number of advanced Category Theoretic concepts, and give simple but powerful examples of their applications.
The first half of the course is dedicated to introducing some categorical structures that are less present in traditional category theory courses, but which are used throughout applications. In particular we will see monoidal categories, string diagrams, enrichments, and profunctors.
In the second half of the course we will put these structures to use, exploring applications of category theory to areas such as computer science, machine learning, quantum computation and probability and information theory (also depending on the students' interests). Some guest experts from these application domains may be invited for specific demonstrations.


Learning outcomes:

The students should get an insight into what it feels like to work with category theoretic structures as well as an idea about how such structures can show up in practice.

Course synopsis:

- Monoidal Categories
- Categories of graphs and enrichments
- Enriched category theory
- Universal properties 
- Example: monads and their use in computer science
- Example: Markov categories in statistics and machine learning
- Example: profunctors and optics
- Example: quantum computation and ZX calculus
(The examples are provisional, depending on the students' interests, and on the guests' availability.)