DD1418 Language Engineering with Introduction to Machine Learning 6.0 credits

Theory:

The historical development and bases of language engineering, morphology, syntax, semantics, vector space models, evaluation methods, machine learning, information theory and Markov models.

Methods::

Morphological analysis, generation and language statistics and corpus processing, parsing, generation, part-of-speech tagging, named entity recognition, probabilistic parsing and statistical lexical semantics.

Application areas:

Spelling and grammar checking, information retrieval, word prediction for smart text entry, text clustering and text categorization, computer-aided language learning, dialogue systems, speech technology and machine translation.

After passing the course, the student shall be able to

1. explain and use basic concepts in linguistics, language engineering and machine learning
2. apply language engineering concepts, methods and tools to build language engineering systems as well as be able to explain the structure of such systems
3. implement standard methods in language engineering
4. design and carry out simple evaluations of a language engineering system as well as interpret the results,
5. independently be able to solve a well delimited practical language engineering problem

in order to be able to

• work with a bachelor's degree project with a focus on language engineering or machine learning,
• be an important link between systems designers, programmers, and interaction designers in industry as well as in research projects.

• Knowledge and skills in programming, 6 higher education credits, equivalent to completed course DD1310/DD1311/DD1312/DD1314/DD1315/DD1316/DD1318/DD1331/DD1337/DD100N/ID1018.
• Knowledge in basic computer science, 6 higher education credits, equivalent to completed course DD1320/DD1321/DD1325/DD1327/DD1338/ID1020/ID1021.
• Knowledge in probability theory, 6 higher education credits, equivalent to completed course SF1912/SF1914-SF1924.

Active participation in a course offering where the final examination is not yet reported in LADOK is considered equivalent to completion of the course. Registering for a course is counted as active participation. The term 'final examination' encompasses both the regular examination and the first re-examination.

If the course is discontinued, students may request to be examined during the following two academic years.

• LAB1 - Laboratory Assignments, 1.5 credits, grading scale: P, F
• PRO1 - Project, 1.5 credits, grading scale: A, B, C, D, E, FX, F
• TEN1 - Exam, 3.0 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Johan Boye

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

For example DD2476 Search Engines and Information Retrieval Systems, DT2112 Speech Technology.

The course is overlapping DD2417. DD1418 is a first-cycle course and has a less advanced project than DD2417. DD1418 cannot be combined with DD2417 or DD2418.

In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex