Machine Learning of Natural Language Processing

Machine Learning of Natural Language Processing

Microcredential

Ignite your skills in artificial intelligence for language technology

Natural Language Processing is an integral component to applications of data science across the technological industry nowadays. Professionals in this domain, however, might struggle to keep up with the fast-paced developments in recent years. In particular the recent and impressive emergence of neural networks in machine learning as a go-to paradigm has rapidly altered the state of the art in artificial intelligence and challenges existing computational approaches to text.

This microcredential has been carved out as a balanced selection of contemporary modules from the MA in Digital Text Analysis that targets industry professionals who are interested in learning about modern machine learning and natural language processing. With an emphasis on project and team work, we develop practical applications on textual data, as well as solutions for the many issues that remain open in the field.

  • Study load: 12 ECTS credits
  • Language of instruction: English
  • ​Maximum number of participants: 5
    • Location: Stadscampus
    • Faculty: Faculty of Arts

    Curriculum

    This microcredential is a targeted subset from the MA in Digital Text Analysis and singles a teaching track in natural language processing.

    None of the modules overlap in time, allowing students to engage at a reasonable pace, over the course of the first and second semester.

    12 ECTS-credits

    Machine Learning I

    This information sheet indicates how the course will be organized at pandemic code level yellow and green.

    If the colour codes change during the academic year to orange or red, modifications are possible, for example to the teaching and evaluation methods.

    Course code:
    2004FLWDTA
    Semester:
    1E SEM
    Contact hours:
    12
    ECTS-credits:
    3 ECTS-credits
    Study load (hours):
    84
    Contract restrictions:
    No contract restriction
    Language of instruction:
    English
    Exam Period:
    exam in the 1st semester
    Study domain:
    Linguistics and Proficiency
    Lecturer(s):
    Machine Learning II

    This information sheet indicates how the course will be organized at pandemic code level yellow and green.

    If the colour codes change during the academic year to orange or red, modifications are possible, for example to the teaching and evaluation methods.

    Course code:
    2006FLWDTA
    Semester:
    1E SEM
    Contact hours:
    12
    ECTS-credits:
    3 ECTS-credits
    Study load (hours):
    84
    Contract restrictions:
    No contract restriction
    Language of instruction:
    English
    Exam Period:
    exam in the 1st semester
    Study domain:
    Linguistics and Proficiency
    Natural Language Processing

    This information sheet indicates how the course will be organized at pandemic code level yellow and green.

    If the colour codes change during the academic year to orange or red, modifications are possible, for example to the teaching and evaluation methods.

    Course code:
    2007FLWDTA
    Semester:
    2E SEM
    Contact hours:
    45
    ECTS-credits:
    6 ECTS-credits
    Study load (hours):
    168
    Contract restrictions:
    No contract restriction
    Language of instruction:
    English
    Exam Period:
    exam in the 2nd semester
    Study domain:
    Linguistics and Proficiency

    Evaluation

    • All courses extensively rely on weekly, hands-on homework assignments, ensuring the acquisition of new, practical insights on a regular basis. The homework takes the form of engaging assignments on real-world datasets that challenge the students to apply the theoretical concept introduced during the interactive class sessions.
    • The final evaluation of all three courses depends on project work, the goal and finality of which can be determined by the individual students, in close correspondence with the course teachers.
    • An attractive feature of the evaluation of the NLP course (in the 2nd semester) is that students will participate in an ongoing shared task in the field.

    Target group and admission requirements

    Professionals active in industry who wish to harness recent advances in AI and Deep Learning in the context of Natural Language Processing for Data Science. Prospective students must have demonstrable programming skills.

    How to enrol

    To take microcredential courses you have to enrol as a credit contract student.

    1. Request the faculty's permission to enrol with a credit contract
    2. Follow the standard enrolment procedure (choose: credit contract > microcredential)

    Tuition fee

    The tuition fee consists of a fixed amount plus an amount per credit. Tuition fees are subject to changes: consult the rates.

    Student account and student card

    You'll have access to all our student facilities: on-campus WiFi, the student portal and online learning platform, your student mailbox, the library, syllabus shops...