At the end of the course students will be able to understand and discuss the principles of logic applied to practical reasoning and AI. They will be able to analyze a problem, and to design and implement a solution. They will be familiar with important techniques in the field and will be able to use them.
Course Prerequisites
Students are expected to have a basic knowledge of linear algebra, vector calculus, logic and probability.
Teaching Methods
This course has two main parts: lectures and exercises. Programming will not be part of this course.
Assessment Methods
The exam is written.
Texts
The course is based on a set of notes that are supplemented by a selection of articles and books.
Contents
After a refresher of logic covering basic concepts of propositional and first-order logic, the course will cover a variety of topics and techniques relating to logic and AI. Topics to be discussed are Modal Logic, Probabilistic Logic, Bayesian Networks.
