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DD1420 Foundations of Machine Learning 7.5 credits

This course is intended as a first course in machine learning (ML) for students intending to become ML-experts. In the course, you will gain a broad overview of the theory and practice in machine learning across different areas and perspectives. Special emphasis is placed on teaching connections and how everything in ML fits together, as well as "learning by doing". A big part of the course is solving machine learning problems and comparing solutions, both on your own and together with your fellow machine-learning experts-to-be at KTH. The goal is to give you a solid theoretical and practical foundation to stand on for taking advanced machine-learning courses at KTH, and to prepare you for a career as a ML practitioner.

Information per course offering

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.

Termin

Information for Spring 2025 GMLVT22 programme students

Course location

KTH Campus

Duration
14 Jan 2025 - 16 Mar 2025
Periods
P3 (7.5 hp)
Pace of study

50%

Application code

60090

Form of study

Normal Daytime

Language of instruction

English

Course memo
Course memo is not published
Number of places

Places are not limited

Target group

Open to students in year 3 and for students admitted to a master's programme as long as it can be included in your programme.

Planned modular schedule
[object Object]
Schedule
Schedule is not published

Contact

Examiner
No information inserted
Course coordinator
No information inserted
Teachers
No information inserted

Course syllabus as PDF

Please note: all information from the Course syllabus is available on this page in an accessible format.

Course syllabus DD1420 (Spring 2025–)
Headings with content from the Course syllabus DD1420 (Spring 2025–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

Important subjects in the course include:

− What is machine learning?
− Optimisation.
− Generalisation.
− Machine Learning theory.
− Neural networks and deep learning.
− Geometry in machine learning.
− Kernel methods.
− Probabilistic methods in machine learning.
− Information theory in machine learning.
− Machine learning for data synthesis.

Intended learning outcomes

After passing the course, the student shall be able to

− use basic concepts, language and notation that supports machine learning
− use mathematical and statistical methods that support machine learning
− derive and prove selected theoretical results
− implement basic machine learning models
− interpret the results to apply machine learning models on data
− discuss how one can solve practical machine learning problems

in order to

− be able to define problems in data analysis clearly
− formulate an appropriate maskinlärningslösning and strengthen this solution through critical and quantitative evaluation
− be well prepared to read advanced courses in machine learning.

Literature and preparations

Specific prerequisites

Knowledge in algebra and geometry, 7.5 higher education credits, equivalent to completed course SF1624.

Knowledge in multivariable analysis, 7.5 higher education credits, equivalent to completed course SF1626.

Knowledge in probability theory and statistics, 7,5 higher education credits, equivalent to completed course SF1912-SF1925/SF1935.

Knowledge and skills in programming, 6 credits, equivalent to completed course DD1337/DD1310-DD1319/DD1321/DD1331/DD100N/ID1018.

Knowledge in algorithms and data structures, at least 6 higher education credits, equivalent to completed course DD1338/DD1320/DD1325/DD1328/DD1338/DD2325/ID1020/ID1021.

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

Equipment

No information inserted

Literature

No information inserted

Examination and completion

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

Grading scale

A, B, C, D, E, FX, F

Examination

  • INLM - Digital assignments with oral comprehension questions, 3.0 credits, grading scale: A, B, C, D, E, FX, F
  • KON1 - Digital quizzes, 3.0 credits, grading scale: A, B, C, D, E, FX, F
  • ÖVN1 - Exercises, 1.5 credits, grading scale: P, 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.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

Ethical approach

  • 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

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

Technology

Education cycle

First cycle

Add-on studies

No information inserted

Transitional regulations

The earlier course module TES1 is replaced by KON1 and PRO1 is replaced by ÖVN1, and INL1 is replaced by INLM.

Supplementary information

The courses DD1420 and DD2421 overlap with regard to their contents. 

One can not recieve credit for both courses.