In this course we will discuss ways of representing the meaning of words, sentences and discourses in a computer so that such representations can be used in language technology tasks. We start with implementing model theoretical semantics for natural language (as developed for example in Montague semantics) in Python. We also look at theorem proving and its application to reasoning in natural language applications. Finally, we will also look at how meaning can be extracted and modelled distributionally by being extracted from large corpora of text (and images).