LEARNING OUTCOMES
The student forms a conceptual understanding of language models and large language models. The student understands key principles underlying the current large language models. The student understands the effect of prompting on large language models and can engineer prompts for large language models to improve output quality. The student knows of issues related to large language models such as hallucination, bias, privacy, and security.
Credits: 1
Schedule: 09.09.2024 - 31.12.2024
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Arto Hellas
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
Language models, probabilistic models, neural networks, large language models, prompting and prompt engineering, issues and concerns related to large language models.
DETAILS
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period: