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Transatlantic US & German State Dual Master of Science — Conferred jointly with Mltio University (USA) & IU International University of Applied Sciences (Germany)
IU International University of Applied Sciences (Germany)
Online or on campus
Rigorous dual Master's coupling US software paradigms with German Industry 4.0 engineering in computer vision, autonomous robotics, and edge intelligence. Students earn 2 independent accredited diplomas with a 50% / 50% international credit split. MSc Applied AI & Autonomous Robotics (US & EU Dual Degree) is taught as one award, not a list of unrelated modules. The published length is 24 months, and the credit total is 60. Students move from Data and measurement through Data and measurement; Models that can be checked; Deployment and care; Analytics project; Candidacy examination; Data and measurement (MSC-802), and finish on Data and measurement (MSC-802). Each course has a question, a method, and a submission. In this field, students define a measurable question, fit a method that can be checked, and report where it fails. Reading is a dataset description, a methods note, and one published evaluation. The artefact a marker expects is a project note with the question, the method, the result, and the limitation. The award is built so that a graduate can do the following in practice: Conferral of both an American MSc and a German state-accredited Master of Science (120 ECTS) Virtual robotics simulation labs with ROS 2, Gazebo, and embedded sensor fusion Full European EQAR recognition and US regional accreditation alignment Teaching assumes the student can read a source, attempt a problem before the seminar, and revise after feedback. Attendance at live seminars is part of the design. The capstone or final course must use the methods of the earlier courses; a project that ignores them does not pass. The pages for each course name the topics that are examined. Those topics are the syllabus. A brochure line is not a substitute for them.
Study online
Online tuition is free. Online examinations are free. There is no exam fee for online study.
On-campus study
Prefer to learn at our Minneapolis campus? On-campus delivery includes tuition fees, in-person seminars, and access to university facilities.
| Code | Course | Credits · hours |
|---|---|---|
| MSC-701 | Data and measurement Data and measurement (MSC-701) is a 6-credit course on MSc Applied AI & Autonomous Robotics (US & EU Dual Degree), with 60 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Data and measurement as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 6 · 60 h |
| MSC-702 | Models that can be checked Models that can be checked (MSC-702) is a 6-credit course on MSc Applied AI & Autonomous Robotics (US & EU Dual Degree), with 60 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Models that can be checked as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 6 · 60 h |
| MSC-703 | Deployment and care Deployment and care (MSC-703) is a 6-credit course on MSc Applied AI & Autonomous Robotics (US & EU Dual Degree), with 60 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Deployment and care as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 6 · 60 h |
| MSC-704 | Analytics project Analytics project (MSC-704) is a 6-credit course on MSc Applied AI & Autonomous Robotics (US & EU Dual Degree), with 60 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Analytics project as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 6 · 60 h |
| Code | Course | Credits · hours |
|---|---|---|
| MSC-801 | Candidacy examination Candidacy examination (MSC-801) is a 0-credit course on MSc Applied AI & Autonomous Robotics (US & EU Dual Degree), with 10 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Candidacy examination as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 0 |
| MSC-802 | Data and measurement (MSC-802) Data and measurement (MSC-802) (MSC-802) is a 36-credit course on MSc Applied AI & Autonomous Robotics (US & EU Dual Degree), with 360 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Data and measurement (MSC-802) as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 36 · 360 h |
Modules are assessed through a published mix of coursework, applied projects, and examinations. Exam windows are announced in advance so students in other time zones are not forced into overnight sittings. Alternative arrangements are available where documented.
The published duration is 24 months. Teaching language: English. Actual time-to-complete depends on mode and any recognised prior learning.
You may study this award fully online from your country, or — where published — on campus at Mltio. Online study does not require a student visa. Campus study may.
Degree tuition for this award is published as £0 / tuition-free on the online pathway. Examination or administrative fees may apply at checkout — never an annual tuition invoice. Check the Fees page for any extras.
Requirements are grouped on this page (academic, English, documents). Equivalent qualifications are considered. English may be waived after prior English-medium study.
Assessment is typically a mix of coursework, projects, and examinations. Doctoral awards include a thesis or dissertation and an oral examination. Details sit in the programme specification and module outlines.
Recognition of the award for local employment, professional licence, or ministry attestation is decided by your employer or regulator. Mltio University publishes verification pages for certificates. We do not claim automatic equivalence in every country.
Start an application on this website. Progress is saved from the first step. Admissions: admissions@mltio.com.
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