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About course

DD1420 Foundations of Machine Learning 7.5 credits

Administer About course

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.

Choose semester and 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.

Application

For course offering

Autumn 2024 GMLht23 programme students

Application code

50244

Headings with content from the Course syllabus DD1420 (Autumn 2021–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

Important subjects in the course include:
•    What is machine learning?
•    Decision making in an uncertain world
•    Optimisation
•    Generalisation
•    Probabilistic methods in machine learning
•    Information theory in machine learning
•    Geometry in machine learning
•    Kernel methods
•    Neural networks and deep learning
•    Machine learning for data synthesis
•    Interactive machine learning
•    Ensemble methods
•    Learning theory
•    Machine learning and the surrounding world

Intended learning outcomes

After passing the course, the student should be able to:

  • use basic concepts, language and notation that supports machine learning
  • explain the mathematical and statistical mechanisms in common machine learning methods.
  • derive and prove selected important theoretical results
  • formulate and implement appropriate machine learning models to solve empirical problems
  • interpret the adaptation of the model to data
  • give an account of strengths and weaknesses of machine learning models/- technologies and justify made choices based on the data
  • identify relevant scientific literature including current trends and make critical assessments of this literature
  • demonstrate critical thinking around ethical and social aspects of machine learning and show awareness of current progress in these fields

in order to

  • be able to define problems in data analysis clearly
  • formulate a suitable solution with machine learning and strengthen this solution through critical and quantitative evaluation
  • be well prepared to read advanced courses in machine learning.

Literature and preparations

Specific prerequisites

Completed courses in all of the following fields:

  • Linear Algebra (SF1624, SF1672, SF1684 or the equivalent)
  • Multivariable analysis (SF1626, SF1674 or the equivalent)
  • Probability and Statistics (SF1912, SF1914-SF1924 or the equivalent)
  • Programming (DD1310, DD1331, DD1337 or the equivalent)
  • Algorithms and Data Structures (DD1320, DD1321, DD1325, DD1327, DD1338, ID1020, ID1021 or the equivalent)

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.

Recommended prerequisites

No information inserted

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

  • INL1 - Assignment, 3.0 credits, grading scale: A, B, C, D, E, FX, F
  • PRO1 - Project work, 1.5 credits, grading scale: P, F
  • TES1 - Quizzes, 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.

Opportunity to complete the requirements via supplementary examination

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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

Supplementary information

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

One can not recieve credit for both courses.