The goal of this course is to become familiar with methods for the processing of natural language.
After attending the course, the student knows how statistical and adaptive methods are used in information retrieval, machine translation, text mining, speech processing 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, Hidden Markov models and Bayesian models.
Many core applications in modern information society such as search engines, social media, machine translation, speech processing and text mining for business intelligence apply statistical and adaptive methods. This course provides information on these methods and teaches basic skills on how they are applied to natural language data. Each topic is handled by a high-level expert in the area.
The requirements for passing the course in 2020 include an exam, a small project group work and home assignments. More information will be given in the first lecture 7.1.
Tuesday, 12:15-14:00 (F239a, Otakaari 3)
Thursday, 14:15-16:00 (Y338, Otakaari 1)
Complete the mandatory entrance test to indicate your intentions and preferences regarding the project work. The deadline to take the test is 14 Jan.