Course syllabus for Industrial immersion in AI and cybersecurity

Course syllabus adopted 2024-03-20 by Head of Programme (or corresponding).

Overview

  • Swedish nameIndustriell erfarenhet inom AI och cybersäkerhet
  • CodeTRA415
  • Credits15 Credits
  • OwnerTRACKS
  • Education cycleSecond-cycle
  • DepartmentTRACKS
  • GradingUG - Pass, Fail

Course round 1

  • Teaching language English
  • Application code 97221
  • Maximum participants12
  • Minimum participants8
  • Open for exchange studentsNo

Credit distribution

0123 Project 15 c
Grading: UG		0 c0 c0 c0 c15 c0 c

In programmes

Examiner

Information missing

Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

Courses in Computer Science, Data Analytics, Machine/Deep Learning, Cyber Security, Mechatronics/embedded systems are considered advantageous.

Aim

The course aims to enable students to develop the ability to apply theoretical knowledge and engineering skills in an industrial context. It focuses on learning to apply these skills to new realistic, complex, and open problem scenarios in a professional, ethical, and sustainable manner.

Additionally, the course aims to train students in developing personal and interpersonal skills to solve advanced problems within interdisciplinary and international teams. It is conducted in an industrial environment to prepare students for a professional career in companies, public organizations, or self-employment.

Learning outcomes (after completion of the course the student should be able to)

General learning outcomes for Tracks courses:
  • master problems with open solutions spaces which includes to be able to handle uncertainties and limited information.
  • lead and participate in the development and assessment of processes and systems using a holistic approach by following a design process and/or a systematic development process.
  • work in multidisciplinary teams and collaborate in teams with different compositions.
  • orally and in writing explain and discuss information, problems, methods, design/development processes and solutions.
Course specific learning outcomes:
  • Carry out a complete product development cycle within an industrial application in AI and/or Cyber Security
  • With sub-goals to be able to:
    • Gather and critically review background materials for projects
    • Formulate functional requirements for an application solution
    • Utilize modern development tools to develop applications within a relevant domain
  • Document, critically review, and present project results
  • Collaborate in an international team of students, academics, and industrial partners

Content

The course is structured around an industrial project idea formulated by a contracted industrial partner, with organizational and academic support from AI Sweden, Dakota State University, and Chalmers.

By the end of the course, the project idea should have resulted in a verified solution and documentation thereof, along with a final report for examination.

Organisation

The course is conducted in collaboration with Chalmers Departments of Computer Science & Engineering and Electrical Engineering, AI Sweden, and a contracted international university (2024 Dakota State University DSU in the USA).

Students are organized into teams with 4-8 students per team, with half of the students coming from the Chalmers course and half from DSU.

Literature

With input from the teaching team, students will develop the ability to identify and acquire relevant literature throughout their projects.

Examination including compulsory elements

PASS/FAIL
Approved joint project report, for each team, where a certain part of the report should be attributable/traceable to individual team members.

Approved joint oral presentation. In the presentation, the industrial partner shall participate and provide feedback.

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.