EP233U Machine Learning in Production 5.0 credits
This course has been discontinued.
Last planned examination: Spring 2022
Decision to discontinue this course:
No information insertedCourse offerings are missing for current or upcoming semesters.
Headings with content from the Course syllabus EP233U (Autumn 2020–) 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
Recommended prerequisites
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Equipment
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Literature
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Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
P, F
Examination
- DEL1 - Workshop, 1.0 credits, grading scale: P, F
- INL1 - Hemuppgifter, 4.0 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.
Opportunity to complete the requirements via supplementary examination
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Opportunity to raise an approved grade via renewed examination
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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
Computer Science and Engineering
Education cycle
Second cycle
Add-on studies
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