Enrolment options

ELEC-E5550 - Statistical Natural Language Processing D, Lecture, 7.1.2025-15.4.2025

Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

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 language modeling including, for instance, clustering, classification, recognition and generation.

Credits: 5

Schedule: 07.01.2025 - 15.04.2025

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

    Independent work approximately

DETAILS

Study Material

  • valid for whole curriculum period:

    C. Manning, H. Schütze, 1999. Foundations of Statistical Natural Language Processing. The MIT Press; Lecture notes.

    D. Jurafsky, J. Martin. Speech and Language Processing 3rd (online) edition. 

Substitutes for Courses
Prerequisites
SDG: Sustainable Development Goals

    3 Good Health and Well-being

    4 Quality Education

    5 Gender Equality

    9 Industry, Innovation and Infrastructure

    10 Reduced Inequality

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language: English

    Teaching Period: 2024-2025 Spring III - IV
    2025-2026 Spring III - IV

Rules of Conduct - Students´ Rights and Responsibilities (in force from 1 August 2020)

Aalto University Code of Academic Integrity and Handling Violations Thereof

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