LEARNING OUTCOMES
After attending the course, the student knows how statistical and deep learning methods are used in language modeling, machine translation, text mining, speech recognition, chatbots and related areas to process natural language contents. Furthermore, the student can apply the basic methods and techniques used for statistical natural language modeling including, for instance, clustering, classification, generation and hidden Markov models.
Credits: 5
Schedule: 11.01.2022 - 12.04.2022
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Mikko Kurimo
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:
Many core applications in modern information society such as search engines, social media, machine translation, speech processing, chatbots and text mining for business intelligence apply statistical and deep learning methods. This course provides information on these methods and teaches basic skills on how they are applied on natural language data.
Assessment Methods and Criteria
valid for whole curriculum period:
Examination, project and exercise work.
Workload
valid for whole curriculum period:
Lectures and excercise sessions approximately 30 h
Independent work approximately 103 h
Total 133 h
DETAILS
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
SDG: Sustainable Development Goals
9 Industry, Innovation and Infrastructure
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 Spring III - IV
2023-2024 Spring III - IV
- Teacher: Kurimo Mikko