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EP236U Machine Learning in Production 5.0 credits

Administer About course

This course introduces to advanced students (proficient in probability theory, linear algebra, and programming) machine learning in production.

Information per course offering

Course offerings are missing for current or upcoming semesters.

Course syllabus as PDF

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

Course syllabus EP236U (Spring 2022–)
Headings with content from the Course syllabus EP236U (Spring 2022–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

  • Lecture 1: Introduction
  • Lecture 2: Data ingestion and analysis
  • Lab 1: Data ingestion and analysis
  • Lecture 3: High-performance machine learning development
  • Lab 2: Model development 
  • Lecture 4: Model deployment and testing
  • Lab 3: Model deployment and testing 
  • Lecture 5: Observability
  • Lab 4: Observability
  • Lecture 6: Privacy and security
  • Lecture 7: Machine learning at the edge

Intended learning outcomes

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

  • summarise a real machine learning ecosystem in production where the model is one of many different components
  • explain the entire end-to-end data pipeline i.e. from data collection and storage to model deployment and monitoring
  • deploy a machine learning model
  • interpret data and handle properties of real data
  • develop batch and online interfaces
  • discuss model versioning and testing
  • evaluate privacy and security in machine learning
  • discuss edge learning and applications of machine learning for Internet of Things

Literature and preparations

Specific prerequisites

  • Knowledge in the equivalent IX1304 of one variable calculus Mathematics 7.5 credits
  • Knowledge in linear algebra equivalent SF1672 Linear Algebra 7.5 credits
  • Knowledge in probability theory equivalent SF2940 Probability Theory 7.5 credits
  • Knowledge in Programming equivalent DD1315 programming and Matlab 7.5 credits
  • The upper secondary course English B/6

Literature

You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.

Examination and completion

Grading scale

P, F

Examination

  • INL1 - Home assignments, 4.0 credits, grading scale: P, F
  • DEL1 - Workshop, 1.0 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.

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

Examiner

Profile picture Carlo Fischione

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

EECS/Computer Science

Main field of study

Computer Science and Engineering

Education cycle

Second cycle