LET-REMA-LCEX33
AI in Language and Communication Research
Course infoSchedule
Course moduleLET-REMA-LCEX33
Credits (ECTS)6
Category-
Language of instructionEnglish
Offered byRadboud University; Faculty of Arts; Linguistics;
Lecturer(s)
Lecturer
dr. I.H.E. Hendrickx
Other course modules lecturer
Examiner
dr. A.M. Liesenfeld
Other course modules lecturer
Lecturer
dr. A.M. Liesenfeld
Other course modules lecturer
Coordinator
dr. A.M. Liesenfeld
Other course modules lecturer
Contactperson for the course
dr. A.M. Liesenfeld
Other course modules lecturer
Academic year2023
Period
PER 1-PER 2  (04/09/2023 to 28/01/2024)
Starting block
PER 1
Course mode
full-time
RemarksOpen for master students Cognitive Neuroscience, Data Science, Artificial Intelligence, Computer Science
Registration using OSIRISYes
Course open to students from other facultiesYes
Pre-registrationNo
Waiting listNo
Placement procedure-
Aims
Upon completion of the course, students are familiar with the main ingredients that make AI-powered language technologies work, such as understanding the structure of language data, key algorithms, and processing pipelines. Equally tailored to language scientists and engineers, the course prepares for both the proficient use of these technologies in language and communication research and serves as a primer to the field of language technology itself.
Content
This course introduces current language and communication technologies, covering a wide range of AI-powered technologies including machine translation, text generation (e.g. ChatGPT), dialogue systems, and social robotics. Looking under the bonnet of such AI language tech, the course aims to foster a deeper understanding of how these technologies work, what their limits are, and enable students to participate in the ongoing discourse about their risks and uses in both academic research and society at large. In a welcoming and collaborative learning environment, the course provides an overview of recent trends in the fields of computational linguistics, speech processing, and human-computer interaction that share the aim to get computers to perform useful tasks involving human language. The course is taught in Python using interactive coding environments. While no prior programming experience is required, the willingness to learn to work with Jupyter notebooks is essential.
Level

Presumed foreknowledge
Experience working with Python is recommended but not an entry requirement.
Test information

Specifics

Instructional modes
Seminar

Tests
Midterm exam
Test weight20
Test typeWritten exam
OpportunitiesBlock PER 1, Block PER 2

Minimum grade
5,5

Final project
Test weight80
Test typeProject
OpportunitiesBlock PER 2, Block PER 3

Minimum grade
5,5