Master's Programme in Computer Science, 120 credits

Masterprogram i datavetenskap, 120 hp

6MICS

Teaching language

English

Campus

Linköping

Degree

Degree of Master of Science (120 credits) with a major in Computer Science

Pace of study

Full-time

Purpose

The Master's Programme in Computer Science aims to educate specialists who will be able to work at the forefront of modern software and computer systems technology in a variety of application areas. The programme also aims at ensuring that the students are well prepared for a career in research or continued studies towards a doctoral degree.
Computer science is one of the most dynamic and expansive fields of science. For the individual scholar or the professional in the field this means that, as well as having a good understanding of the theoretical and technical foundations of the field, one needs to be able to apply the technology to new challenging problems and integrate it with other technologies. 

Aim

The Master's Programme in Computer Science offers the opportunity for advanced study in computer science and engineering and allows for flexible planning of specialization areas. Having covered core computer science courses, the students will be able to focus their studies on areas such as artificial intelligence, databases and data-mining, internet computing, embedded systems, information security, design and programming computer games, language technology, human-computer interaction, theoretical computer science or design and implementation of computer languages.

The following learning outcomes will serve as measurable goals towards the implementation of the general aim of the program. These objectives are formulated in terms of capacities competences of the students who successfully complete the program.

  • A Computer Science master will be able to understand and apply mathematical concepts which are necessary for modelling various kinds of computational problems. He/She will have an understanding of both software and hardware issues.
  • A Computer Science master will be a competent programmer who is familiar with a variety of programming languages and tools and is able to creatively apply his/her knowledge and skills to modelling and developing software solutions which contribute towards applications in a wide variety of application domains.
  • The Computer Science master will be able to work as a team member and effectively cooperate with other specialists and contribute towards the solution of complex technical problems.
  • The Computer Science master will be qualified to take a leading role in a software design and development team, evaluate and compare solutions, and decision making. He/She will be able to further deepen his/her knowledge and contribute to the development of the area.
  • The Computer Science master will be a good communicator who will be able to present coherent technical and scientific results both orally and in writing.
  • Students who successfully complete the programme will have a good understanding of the impact of computers in society, ethical issues relevant to the field, as well as the responsibilities of the computer science professionals.
  • Although computer science enjoys a relatively stable scientific foundation, the field is still dynamic and expansive. An important aspect of educational programs in the field is to prepare the students for a lifelong learning in the field.

Content

The programme is based on fundamental mathematical, theoretical, and technical knowledge acquired by the student during his/her undergraduate education. This basic knowledge should cover programming in various languages and paradigms, algorithms, databases, system software, operating systems and mathematical knowledge which should include discrete mathematics, logic and statistics.
The Master's Programme in Computer Science is both theoretical and applied. A number of courses will provide the student with the broad view and understanding needed in order to master the general area. At the same time, a proper selection of courses allows for further specialisation.
Communication skills, presentation techniques (both oral and written), as well as team work, are emphasized during the whole program.
Each year the programme board decides what courses will be given and included in the programme. This is found in the curriculum. For each course there is a course syllabus, describing the learning outcomes, organisation, examination and the classification of the advancement level and to what subject area the course belongs. The course advancement level and subject area are important in fulfilling the requirements for the Master's degree. 

Education profiles

The specialisation areas are visible in the curriculum. A specialisation shall be fulfilled and the name of the specialisation will be included in the Degree Certificate.

Specialisation areas in the programme include:

  • AI and Machine Learning
  • Visualization and Computer Graphics
  • Computer Networks, Distributed Systems and Security
  • Programming and Software Methods

In order to meet the specialization requirement, 36 hp of the courses in the degree must be within the specialization.

Teaching and working methods

The programme is campus-based.

Entry requirements

  • A bachelor's degree equivalent to a Swedish Kandidatexamen with a major in one of the following or equivalent subject areas:
    -computer science 
    -information technology
    -software engineering
    -computer engineering
    Or
    A bachelor's degree equivalent to a Swedish Kandidatexamen with a minor in computer science or related subject area, with a minimum of 60 ECTS credits in computer-related subjects equivalent to:
    -programming
    -data structures
    -databases
    -software engineering
    -computer hardware
    -computer networks
  • At least 24 ECTS credits in mathematics/applied mathematics and/or application of mathematics relevant for the programme including courses in discrete mathematics, linear algebra and calculus.
  • English corresponding to the level of English in Swedish upper secondary education (Engelska 6 or Engelska nivå 2).
    Exemption from Swedish.

    Degree thesis

    The thesis encompasses independent work corresponding to 30 ECTS credits. The students are encouraged to carry out their thesis work in their specialisation area. The thesis should be written and presented in English. The thesis work should be supervised by a faculty member within computer science and engineering.

    Degree requirements

    The requirements are the following:

    • a Bachelor's degree as specified in the entrance requirements.
    • course requirements for a total of 120 ECTS credits from courses from the curriculum of the programme, or after special decision from the programme board, and thesis work.
    • passed the requirements for all compulsory courses.
    • requirements for one specialisation fulfilled.
    • courses on advancement level A (advanced) 90 ECTS credits including:
      • at least 30 ECTS credits courses from the main field of study of Computer Science.
      • a 30 ECTS credits Master's Thesis in the main field of study of Computer Science.
    • a Master's thesis presented and passed as per Linköping Institute of Technology degree regulations.
    • One of the following courses must be completed and approved:
      • TDDE79 Imperative Programming in C++
      • TDDE18 Programming C++
      • TDDD38 Advanced C++
    • One of the following courses must be completed and approved:
      • TDDD89 Scientific Method
      • TNM107 Scientific Method

    Courses overlapping each other regarding contents are not allowed to be included in the degree. Courses used for the Bachelor's degree can never be included in the Master's degree but can, after admitted application to the Programme board, fulfill a course requirement for the programme.


    About the Degree
    Students who have studied advanced courses in computer science prior to the Master's programme can either transfer some of their credits to the programme or be allowed to substitute compulsory courses in the programme with other courses. Transferring credits is only applicable to earlier courses that have not been included in other degrees.

    Degree in Swedish

    Teknologie masterexamen med huvudområde Datavetenskap

    Degree in English

    Degree of Master of Science (120 credits) with a major in Computer Science

    Specific information

    Graduate Level Courses
    Certain PhD courses can be taken by master students. These course selections are subject to formal decision by the executive committee of the Programme Board.

     

    Entrance requirements

    See general rules and regulations for master programmes at LiTH.

    Common rules

    See the Common rules tab regarding eligibility, admission, leave, postponement, study breaks or admission to later part of the education program.

    Deviations from programme syllabus

    If special circumstances prevail, the vice-chancellor may in a special decision specify the preconditions for temporary deviations from this programme syllabus, and delegate the right to take such decisions.

    Semester 1 Autumn 2026

    Course code Course name Credits Level Timetable module ECV
    Period 1
    TDDE80 Professionalism in Computer Science 6* A1N 4 C
    *The course is divided into several semesters and/or periods
    TDDD38 Advanced Programming in C++ 6* A1N 2 C/E
    *The course is divided into several semesters and/or periods
    For the Master's Programme in Computer Science one of TDDE18, TDDD38 or TDDE79 shall be completed or have been included in a previous bachelor's degree. TDDD38 can be studied in semester 1,2 or 3.
    TDDE18 Programming C++ 6* G2F 2 C/E
    *The course is divided into several semesters and/or periods
    For the Master's Programme in Computer Science one of TDDE18, TDDD38 or TDDE79 shall be completed or have been included in a previous bachelor's degree.
    TDDE79 Imperative programming in C++ 6* G2F 2 C/E
    *The course is divided into several semesters and/or periods
    For the Master's Programme in Computer Science one of TDDE18, TDDD38 or TDDE79 shall be completed.
    TDDC17 Artificial Intelligence 6 G2F 3 E
    TDTS06 Computer Networks 6 G2F 1 E
    TDTS08 Advanced Computer Architecture 6 A1N 2 E
    Period 2
    TAMS11 Probability and Statistics, First Course 6 G2F 4 C
    The course can also be taken in semester 2. 6MICS students with course/s corresponding to ”TAMS11 Probability and Statistics” in BSc Degree can apply to remove mandatory requirement for the course in MSc programme degree. Contact Study Counselor.
    TDDE80 Professionalism in Computer Science 6* A1N 3 C
    *The course is divided into several semesters and/or periods
    TDDD38 Advanced Programming in C++ 6* A1N 1 C/E
    *The course is divided into several semesters and/or periods
    For the Master's Programme in Computer Science one of TDDE18, TDDD38 or TDDE79 shall be completed or have been included in a previous bachelor's degree. TDDD38 can be studied in semester 1,2 or 3.
    TDDE18 Programming C++ 6* G2F 1 C/E
    *The course is divided into several semesters and/or periods
    For the Master's Programme in Computer Science one of TDDE18, TDDD38 or TDDE79 shall be completed or have been included in a previous bachelor's degree.
    TDDE79 Imperative programming in C++ 6* G2F 1 C/E
    *The course is divided into several semesters and/or periods
    For the Master's Programme in Computer Science one of TDDE18, TDDD38 or TDDE79 shall be completed.
    TDDC34 Technical, Economic and Societal Evaluation of IT-products 6 A1N 4 E
    TDDD07 Real Time Systems 6 A1N 4 E
    TDDD37 Database Technology 6 G2F 1 E
    TDDE01 Machine Learning 6 A1N 1 E
    TDDE66 Compiler Construction 6 A1N 1 E
    TSIT02 Computer Security 6 G2F 2 E
    Specialisation: AI and Machine Learning
    Course code Course name Credits Level Timetable module ECV
    Period 1
    TDDC17 Artificial Intelligence 6 G2F 3 C
    Period 2
    TDDE01 Machine Learning 6 A1N 1 C
    For students who must take other compulsory courses in the scheduled block, this course is taken instead in semester 3.
    Specialisation: Computer Networks, Distributed Systems and Security
    Course code Course name Credits Level Timetable module ECV
    Period 1
    TDTS06 Computer Networks 6 G2F 1 C
    Students with TDTS04 or TDTS11 in their bachelor's degree choose another course from the specialization to meet the requirements.
    Period 2
    TSIT02 Computer Security 6 G2F 2 C
    Specialisation: Programming and Software Methods
    Course code Course name Credits Level Timetable module ECV
    Period 1
    TDDD38 Advanced Programming in C++ 6* A1N 2 E
    *The course is divided into several semesters and/or periods
    Period 2
    TDDC34 Technical, Economic and Societal Evaluation of IT-products 6 A1N 4 E
    For students who must take other compulsory courses in the scheduled block, this course can be taken in semester 3.
    TDDD38 Advanced Programming in C++ 6* A1N 1 E
    *The course is divided into several semesters and/or periods
    TDDE66 Compiler Construction 6 A1N 1 E

    Semester 2 Spring 2027

    Preliminary courses
    Course code Course name Credits Level Timetable module ECV
    Period 1
    TAMS11 Probability and Statistics, First Course 6 G2F 4 C/E
    The course also can be taken in semester 1
    TATA54 Number Theory 6* G2F 2 E
    *The course is divided into several semesters and/or periods
    TATA64 Graph Theory 6* A1N 2 E
    *The course is divided into several semesters and/or periods
    TBMI26 Neural Networks and Learning Systems 6 A1N 2 E
    TDDD20 Design and Analysis of Algorithms 6 A1N 3 E
    Note that the course is given in Swedish. English-speaking students, please contact the examiner of the course three months before course start for guidance.
    TDDD25 Distributed Systems 6 A1N 2 E
    TDDD38 Advanced Programming in C++ 6* A1N 2 E
    *The course is divided into several semesters and/or periods
    TDDD41 Data Mining - Clustering and Association Analysis 6 A1N 3 E
    TDDD95 Algorithmic Problem Solving 6* A1F 1 E
    *The course is divided into several semesters and/or periods
    TDDD97 Web Programming 6 G2F 3 E
    TDDE05 AI Robotics 6* A1N 4 E
    *The course is divided into several semesters and/or periods
    TDDE09 Natural Language Processing 6 A1F 2 E
    TDDE51 Methods and Tools for Large Distributed Projects 6* A1N 4 E
    *The course is divided into several semesters and/or periods
    TDDE62 Information Security: Privacy, System and Network Security 6 A1N 4 E
    TDDE68 Concurrent Programming and Operating Systems 6 G2F 3 E
    TDTS07 System Design and Methodology 6 A1N 1 E
    TDTS21 Advanced Networking 6* A1N 1 E
    *The course is divided into several semesters and/or periods
    TNM061 3D Computer Graphics 6* G2F 1 E
    *The course is divided into several semesters and/or periods
    TNM111 Information Visualization 6 A1N 3 E
    TSBK38 Image and Audio Compression 6 A1N 4 E
    Period 2
    TAOP24 Optimization, Advanced Course 6 G2F 1 E
    TATA54 Number Theory 6* G2F 2 E
    *The course is divided into several semesters and/or periods
    TATA64 Graph Theory 6* A1N 2 E
    *The course is divided into several semesters and/or periods
    TDDD14 Formal Languages and Automata Theory 6 G2F 2 E
    TDDD27 Advanced Web Programming 6 A1N 3 E
    TDDD38 Advanced Programming in C++ 6* A1N 1 E
    *The course is divided into several semesters and/or periods
    TDDD48 Automated Planning 6 A1N 1 E
    TDDD95 Algorithmic Problem Solving 6* A1F 4 E
    *The course is divided into several semesters and/or periods
    TDDE05 AI Robotics 6* A1N 4 E
    *The course is divided into several semesters and/or periods
    TDDE07 Bayesian Learning 6 A1F 2 E
    TDDE31 Big Data Analytics 6 A1F 3 E
    TDDE34 Software Verification 6 A1N 1 E
    TDDE41 Software Architectures 6 A1N 1 E
    TDDE51 Methods and Tools for Large Distributed Projects 6* A1N 4 E
    *The course is divided into several semesters and/or periods
    TDDE64 Sports Analytics 6 A1N 3 E
    TDDE65 Programming of Parallel Computers - Methods and Tools 6 A1N 2 E
    TDDE70 Deep Learning 6 A1F 1 E
    TDTS21 Advanced Networking 6* A1N 1 E
    *The course is divided into several semesters and/or periods
    TNM061 3D Computer Graphics 6* G2F 1 E
    *The course is divided into several semesters and/or periods
    TNM079 Modelling and Animation 6 A1N 2 E
    TNM096 Artificial Intelligence - Principles and Techniques 6 G2F 1 E
    TNM098 Advanced Visual Data Analysis 6 A1N 4 E
    Specialisation: AI and Machine Learning — Preliminary courses
    Course code Course name Credits Level Timetable module ECV
    Period 1
    TBMI26 Neural Networks and Learning Systems 6 A1N 2 E
    TDDD41 Data Mining - Clustering and Association Analysis 6 A1N 3 E
    TDDE05 AI Robotics 6* A1N 4 E
    *The course is divided into several semesters and/or periods
    TDDE09 Natural Language Processing 6 A1F 2 E
    Period 2
    TDDD48 Automated Planning 6 A1N 1 E
    TDDE05 AI Robotics 6* A1N 4 E
    *The course is divided into several semesters and/or periods
    TDDE07 Bayesian Learning 6 A1F 2 E
    TDDE31 Big Data Analytics 6 A1F 3 E
    TDDE64 Sports Analytics 6 A1N 3 E
    TDDE70 Deep Learning 6 A1F 1 E
    Specialisation: Computer Networks, Distributed Systems and Security — Preliminary courses
    Course code Course name Credits Level Timetable module ECV
    Period 1
    TDDD25 Distributed Systems 6 A1N 2 E
    TDDE62 Information Security: Privacy, System and Network Security 6 A1N 4 E
    TDTS21 Advanced Networking 6* A1N 1 E
    *The course is divided into several semesters and/or periods
    Period 2
    TDTS21 Advanced Networking 6* A1N 1 E
    *The course is divided into several semesters and/or periods
    Specialisation: Programming and Software Methods — Preliminary courses
    Course code Course name Credits Level Timetable module ECV
    Period 1
    TDDD25 Distributed Systems 6 A1N 2 E
    TDDD38 Advanced Programming in C++ 6* A1N 2 E
    *The course is divided into several semesters and/or periods
    TDDD97 Web Programming 6 G2F 3 E
    TDDE51 Methods and Tools for Large Distributed Projects 6* A1N 4 E
    *The course is divided into several semesters and/or periods
    TDDE68 Concurrent Programming and Operating Systems 6 G2F 3 E
    Period 2
    TDDD27 Advanced Web Programming 6 A1N 3 E
    TDDD38 Advanced Programming in C++ 6* A1N 1 E
    *The course is divided into several semesters and/or periods
    TDDE34 Software Verification 6 A1N 1 E
    TDDE41 Software Architectures 6 A1N 1 E
    TDDE51 Methods and Tools for Large Distributed Projects 6* A1N 4 E
    *The course is divided into several semesters and/or periods
    TDDE65 Programming of Parallel Computers - Methods and Tools 6 A1N 2 E
    Specialisation: Visualization and Computer Graphics (semester 2, 3 at Campus Norrköping) — Preliminary courses
    Course code Course name Credits Level Timetable module ECV
    Period 1
    TNM061 3D Computer Graphics 6* G2F 1 E
    *The course is divided into several semesters and/or periods
    TNM111 Information Visualization 6 A1N 3 E
    TSBK38 Image and Audio Compression 6 A1N 4 E
    Period 2
    TNM061 3D Computer Graphics 6* G2F 1 E
    *The course is divided into several semesters and/or periods
    TNM079 Modelling and Animation 6 A1N 2 E
    TNM096 Artificial Intelligence - Principles and Techniques 6 G2F 1 E
    TNM098 Advanced Visual Data Analysis 6 A1N 4 E

    Semester 3 Autumn 2027

    Preliminary courses
    Course code Course name Credits Level Timetable module ECV
    Period 1
    TAMS43 Probability Theory and Bayesian Networks 6 A1N 1 E
    TATA55 Abstract Algebra 6* G2F 3 E
    *The course is divided into several semesters and/or periods
    TBMI19 Medical Information Systems 6* A1N 2 E
    *The course is divided into several semesters and/or periods
    TDDC88 Software Engineering 12* A1N 1 E
    *The course is divided into several semesters and/or periods
    TDDD04 Software Testing 6 A1N 2 E
    TDDD08 Logic Programming 6 A1N 4 E
    TDDD23 Design and Programming of Computer Games 6 A1N 2 E
    TDDD38 Advanced Programming in C++ 6* A1N 2 E
    *The course is divided into several semesters and/or periods
    TDDD43 Advanced Data Models and Databases 6* A1N 2 E
    *The course is divided into several semesters and/or periods
    TDDE15 Advanced Machine Learning 6 A1F 1 E
    TDDE19 Advanced Project Course - AI and Machine Learning 6* A1F 4 E
    *The course is divided into several semesters and/or periods
    TDDE21 Advanced Project Course: Secure Distributed and Embedded Systems 6* A1F 4 E
    *The course is divided into several semesters and/or periods
    TDDE45 Software Design and Construction 6 A1N 4 E
    TDDE52 Programming Project with Open Source Code 6* A1F 4 E
    *The course is divided into several semesters and/or periods
    TDDE58 Wireless Connectivity 6 A1N 2 E
    TDTS06 Computer Networks 6 G2F 1 E
    TDTS08 Advanced Computer Architecture 6 A1N 2 E
    TNCG15 Advanced Global Illumination and Rendering 6 A1N 4 E
    TNM067 Scientific Visualization 6 A1N 3 E
    TNM091 Media Production for Immersive Environments 6* A1N 2 E
    *The course is divided into several semesters and/or periods
    TNM114 Artificial Intelligence for Interactive Media, Project 6 A1N 2 E
    TSIN01 Information Networks 6 A1N 3 E
    TSIT03 Cryptology 6 A1N 2 E
    Period 2
    TDDD89 Scientific Method 6 A1F 3 C/E
    For the Master's programme in Computer Science, either TDDD89 or TNM107 must be included in the degree. Select TNM107 for the “Visualization and Computer Graphics” specialization. The course is replaced by an optional course for students with TDIU14 or TDP026 in Bachelor's degree.
    TNM107 Scientific Method 6 A1F 3 C/E
    For the Master's program Computer Science, either TDDD89 or TNM107 must be included in the degree. Select TNM107 for the “Visualization and Computer Graphics” specialization. The course is replaced by an optional course for students with TDIU14 or TDP026 in Bachelor's degree.
    TATA55 Abstract Algebra 6* G2F 3 E
    *The course is divided into several semesters and/or periods
    TBMI19 Medical Information Systems 6* A1N 3 E
    *The course is divided into several semesters and/or periods
    TDDC34 Technical, Economic and Societal Evaluation of IT-products 6 A1N 4 E
    TDDC88 Software Engineering 12* A1N 1 E
    *The course is divided into several semesters and/or periods
    TDDC90 Software Security 6 A1N 1 E
    TDDD07 Real Time Systems 6 A1N 4 E
    TDDD38 Advanced Programming in C++ 6* A1N 1 E
    *The course is divided into several semesters and/or periods
    TDDD43 Advanced Data Models and Databases 6* A1N 2 E
    *The course is divided into several semesters and/or periods
    TDDD56 Multicore and GPU Programming 6 A1N 2 E
    TDDE01 Machine Learning 6 A1N 1 E
    TDDE13 Multi Agent Systems 6 A1N 1 E
    TDDE16 Text Mining 6 A1F 2 E
    TDDE19 Advanced Project Course - AI and Machine Learning 6* A1F 4 E
    *The course is divided into several semesters and/or periods
    TDDE21 Advanced Project Course: Secure Distributed and Embedded Systems 6* A1F 4 E
    *The course is divided into several semesters and/or periods
    TDDE52 Programming Project with Open Source Code 6* A1F 4 E
    *The course is divided into several semesters and/or periods
    TDDE66 Compiler Construction 6 A1N 1 E
    TNM084 Procedural Methods for Images 6 A1N 4 E
    TNM091 Media Production for Immersive Environments 6* A1N 1 E
    *The course is divided into several semesters and/or periods
    TNM116 eXtended Reality (XR) - Principles and Programming 6 A1N 2 E
    TSIN02 Internetworking 6 A1N 1 E
    TSKS33 Complex Networks and Big Data 6 A1N 2 E
    Specialisation: AI and Machine Learning — Preliminary courses
    Course code Course name Credits Level Timetable module ECV
    Period 1
    TDDE19 Advanced Project Course - AI and Machine Learning 6* A1F 4 C
    *The course is divided into several semesters and/or periods
    TDDD43 Advanced Data Models and Databases 6* A1N 2 E
    *The course is divided into several semesters and/or periods
    TDDE15 Advanced Machine Learning 6 A1F 1 E
    Period 2
    TDDE19 Advanced Project Course - AI and Machine Learning 6* A1F 4 C
    *The course is divided into several semesters and/or periods
    TDDD43 Advanced Data Models and Databases 6* A1N 2 E
    *The course is divided into several semesters and/or periods
    TDDE13 Multi Agent Systems 6 A1N 1 E
    TDDE16 Text Mining 6 A1F 2 E
    TSKS33 Complex Networks and Big Data 6 A1N 2 E
    Specialisation: Computer Networks, Distributed Systems and Security — Preliminary courses
    Course code Course name Credits Level Timetable module ECV
    Period 1
    TDDE21 Advanced Project Course: Secure Distributed and Embedded Systems 6* A1F 4 E
    *The course is divided into several semesters and/or periods
    TDDE58 Wireless Connectivity 6 A1N 2 E
    TDTS06 Computer Networks 6 G2F 1 E
    TSIN01 Information Networks 6 A1N 3 E
    TSIT03 Cryptology 6 A1N 2 E
    Period 2
    TDDC90 Software Security 6 A1N 1 E
    TDDE21 Advanced Project Course: Secure Distributed and Embedded Systems 6* A1F 4 E
    *The course is divided into several semesters and/or periods
    TSIN02 Internetworking 6 A1N 1 E
    Specialisation: Programming and Software Methods — Preliminary courses
    Course code Course name Credits Level Timetable module ECV
    Period 1
    TDDC88 Software Engineering 12* A1N 1 E
    *The course is divided into several semesters and/or periods
    TDDD04 Software Testing 6 A1N 2 E
    TDDD08 Logic Programming 6 A1N 4 E
    TDDE45 Software Design and Construction 6 A1N 4 E
    TDDE52 Programming Project with Open Source Code 6* A1F 4 E
    *The course is divided into several semesters and/or periods
    Period 2
    TDDC34 Technical, Economic and Societal Evaluation of IT-products 6 A1N 4 E
    TDDC88 Software Engineering 12* A1N 1 E
    *The course is divided into several semesters and/or periods
    TDDC90 Software Security 6 A1N 1 E
    TDDD56 Multicore and GPU Programming 6 A1N 2 E
    TDDE52 Programming Project with Open Source Code 6* A1F 4 E
    *The course is divided into several semesters and/or periods
    TDDE66 Compiler Construction 6 A1N 1 E
    Specialisation: Visualization and Computer Graphics (semester 2, 3 at Campus Norrköping) — Preliminary courses
    Course code Course name Credits Level Timetable module ECV
    Period 1
    TNCG15 Advanced Global Illumination and Rendering 6 A1N 4 E
    TNM067 Scientific Visualization 6 A1N 3 E
    TNM091 Media Production for Immersive Environments 6* A1N 2 E
    *The course is divided into several semesters and/or periods
    TNM114 Artificial Intelligence for Interactive Media, Project 6 A1N 2 E
    Period 2
    TNM084 Procedural Methods for Images 6 A1N 4 E
    TNM091 Media Production for Immersive Environments 6* A1N 1 E
    *The course is divided into several semesters and/or periods
    TNM116 eXtended Reality (XR) - Principles and Programming 6 A1N 2 E

    Semester 4 Spring 2028

    Preliminary courses
    Course code Course name Credits Level Timetable module ECV
    Period 1
    TQXX30 Degree project - Master’s Thesis 30* A2E - C
    *The course is divided into several semesters and/or periods
    Period 2
    TQXX30 Degree project - Master’s Thesis 30* A2E - C
    *The course is divided into several semesters and/or periods

    Course syllabus

    A syllabus must be established for each course. The syllabus specifies the aim and contents of the course, and the prior knowledge that a student must have in order to be able to benefit from the course.

    Timetabling

    Program courses are timetabled after a decision has been made for this course concerning its assignment to a timetable module. Single subject courses can be timetabled at other times.

    Interruption in and deregistration from a course

    The LiU decision, Guidelines concerning confirmation of participation in education, Dnr LiU-2020-02256 (https://styrdokument.liu.se/Regelsamling/VisaBeslut/764582), states that interruptions in study are to be recorded in Ladok. Thus, all students who do not participate in a course for which they have registered are therefore obliged to report the interruption so that this can be noted in Ladok. Deregistration from or interrupting a course is carried out using a Web-based form.

    Cancelled courses and changes to the course syllabus

    Courses with few participants (fewer than 10) may be cancelled or organised in a manner that differs from that stated in the course syllabus. The Dean is to deliberate and decide whether a course is to be cancelled or changed from the course syllabus. For single subject courses, the cancellation must be done before students are admitted to the course (in accordance with LiUs regulation Dnr LiU-2022-01200, https://styrdokument.liu.se/Regelsamling/VisaBeslut/622645).

    Guidelines relating to examinations and examiners 

    For details, see Guidelines for education and examination for first-cycle and second-cycle education at Linköping University, Dnr LiU-2023-00379  (http://styrdokument.liu.se/Regelsamling/VisaBeslut/917592).

    An examiner must be employed as a teacher at LiU according to the LiU Regulations for Appointments, Dnr LiU-2022-04445 (https://styrdokument.liu.se/Regelsamling/VisaBeslut/622784). For courses in second-cycle, the following teachers can be appointed as examiner: Professor (including Adjunct and Visiting Professor), Associate Professor (including Adjunct), Senior Lecturer (including Adjunct and Visiting Senior Lecturer), Research Fellow, or Postdoc. For courses in first-cycle, Assistant Lecturer (including Adjunct and Visiting Assistant Lecturer) can also be appointed as examiner in addition to those listed for second-cycle courses. In exceptional cases, a Part-time Lecturer can also be appointed as an examiner at both first- and second cycle, see Delegation of authority for the Board of Faculty of Science and Engineering.

    Forms of examination

    Principles for examination

    Written and oral examinations and digital and computer-based examinations are held at least three times a year: once immediately after the end of the course, once in August, and once (usually) in one of the re-examination periods. Examinations held at other times are to follow a decision of the faculty programme board.

    Principles for examination scheduling for courses that follow the study periods:

    • courses given in VT1 are examined for the first time in March, with re-examination in June and August
    • courses given in VT2 are examined for the first time in May, with re-examination in August and January
    • courses given in HT1 are examined for the first time in October, with re-examination in January and August
    • courses given in HT2 are examined for the first time in January, with re-examination in March and in August.

    The examination schedule is based on the structure of timetable modules, but there may be deviations from this, mainly in the case of courses that are studied and examined for several programmes and in lower grades (i.e. 1 and 2). 

    Examinations for courses that the faculty programme board has decided are to be held in alternate years are held three times during the school year in which the course is given according to the principles stated above.

    Examinations for courses that are cancelled or rescheduled such that they are not given in one or several years are held three times during the year that immediately follows the course, with examination scheduling that corresponds to the scheduling that was in force before the course was cancelled or rescheduled.

    When a course, or a written or oral examination (TEN, DIT, DAT, MUN), is given for the last time, the regular examination and two re-examinations will be offered. Thereafter, examinations are phased out by offering three examinations during the following academic year at the same times as the examinations in any substitute course. The exception is courses given in the period HT1, where the three examination occasions are January, March and August. If there is no substitute course, three examinations will be offered during re-examination periods during the following academic year. Other examination times are decided by the faculty programme board. In all cases above, the examination is also offered one more time during the academic year after the following, unless the faculty programme board decides otherwise. In total, 6 re-examinations are offered, of which 2 are regular re-examinations. In the examination registration system, the examinations given for the penultimate time and the last time are denoted. 

    If a course is given during several periods of the year (for programmes, or on different occasions for different programmes) the faculty programme board or boards determine together the scheduling and frequency of re-examination occasions.

    For single subject courses, written and oral examinations can be held at other times.  

    Retakes of other forms of examination

    Regulations concerning retakes of other forms of examination than written examinations and digital and computer-based examinations are given in the LiU guidelines for examinations and examiners, Dnr LiU-2023-00379 (http://styrdokument.liu.se/Regelsamling/VisaBeslut/917592).

    In principle, other examination forms should be handled in the same way as a written examination when they are given for the last time. However, the times for the examination may vary based on the nature of the element compared to the times for the written examinations. 

    Course closure

    For Decision on Routines for Administration of the Discontinuation of Educational Programs, Freestanding Courses and Courses in Programs, see Dnr LiU-2021-04782 (https://styrdokument.liu.se/Regelsamling/VisaBeslut/1156410). After a decision on closure and after the end of the discontinuation period, the students are referred to a replacement course (or similar) according to information in the course syllabus or programme syllabus. If a student has passed some part/parts of a closed program course but not all, and there is an at least partially replacing course, an assessment of crediting can be made. For questions about the crediting of course components, contact the Study councellors.

    Registration for examination

    In order to take an written, digital or computer-based examination, registration in advance is mandatory, see decision in the university’s rule book Dnr LiU-2020-04559 (https://styrdokument.liu.se/Regelsamling/VisaBeslut/622682). An unregistered student can thus not be offered a place. The registration is done by the student at the Student Portal or in the LiU-app during the registration period. The registration period opens 30 days before the date of the examination and closes 10 days before the date of the examination. Candidates are informed of the location of the examination by email, four days in advance. 

    Code of conduct for students during examinations

    Details are given in a decision in the university’s rule book, Dnr LiU-2020-04559 (http://styrdokument.liu.se/Regelsamling/VisaBeslut/622682).

    Retakes for higher grade

    Students at the Faculty of Science and Engineering at LiU have the right to retake written examinations and digital and computer-based examinations in an attempt to achieve a higher grade. This is valid for all examination components with code “TEN”, “DIT” and "DAT". The same right may not be exercised for other examination components, unless otherwise specified in the course syllabus.

    A retake is not possible on courses that are included in an issued degree diploma. 

    Grades

    The grades that are preferably to be used are Fail (U), Pass (3), Pass not without distinction (4) and Pass with distinction (5). 

    • Grades U, 3, 4, 5 are to be awarded for courses that have written or digital examinations.
    • Grades Fail (U) and Pass (G) may be awarded for courses with a large degree of practical components such as laboratory work, project work and group work.
    • Grades Fail (U) and Pass (G) are to be used for degree projects and other independent work.

    Examination components

    The following examination components and associated module codes are used at the Faculty of Science and Engineering:

    • Grades U, 3, 4, 5 are to be awarded for written examinations (TEN) and digital examinations (DIT).
    • Examination components for which the grades Fail (U) and Pass (G) may be awarded are laboratory work (LAB), project work (PRA), preparatory written examination (KTR), digital preparatory written examination (DIK), oral examination (MUN), computer-based examination  in a computer lab (DAT), digital preparatory written examination in a computer lab (DAK), home assignment (HEM), and assignment (UPG).
    • Students receive grades either Fail (U) or Pass (G) for other examination components in which the examination criteria are satisfied principally through active attendance such as tutorial group (BAS) or examination item (MOM).
    • Grades Fail (U) and Pass (G) are to be used for the examination components Opposition (OPPO) and Attendance at thesis presentation (AUSK) (i.e. part of the degree project).

    In general, the following applies:

    • Mandatory course components must be scored and given a module code.
    • Examination components that are not scored, cannot be mandatory. Hence, it is voluntary to participate in these examinations, and the voluntariness must be clearly stated. Additionally, if there are any associated conditions to the examination component, these must be clearly stated as well.
    • For courses with more than one examination component with grades U,3,4,5, it shall be clearly stated how the final grade is weighted.

    For mandatory components, the following applies (in accordance with the LiU Guidelines for education and examination for first-cycle and second-cycle education at Linköping University, Dnr LiU-2023-00379 http://styrdokument.liu.se/Regelsamling/VisaBeslut/917592): 

    • If special circumstances prevail, and if it is possible with consideration of the nature of the compulsory component, the examiner may decide to replace the compulsory component with another equivalent component.

    For possibilities to alternative forms of examinations, the following applies (in accordance with the LiU Guidelines for education and examination for first-cycle and second-cycle education at Linköping University, Dnr LiU-2023-00379 http://styrdokument.liu.se/Regelsamling/VisaBeslut/917592): 

    • If the LiU coordinator for students with disabilities has granted a student the right to an adapted examination for a written examination in an examination hall, the student has the right to it.
    • If the coordinator has recommended for the student an adapted examination or alternative form of examination, the examiner may grant this if the examiner assesses that it is possible, based on consideration of the course objectives.
    • An examiner may also decide that an adapted examination or alternative form of examination if the examiner assessed that special circumstances prevail, and the examiner assesses that it is possible while maintaing the objectives of the course.

    Reporting of examination results

    The examination results for a student are reported at the relevant department.

    Plagiarism

    For examinations that involve the writing of reports, in cases in which it can be assumed that the student has had access to other sources (such as during project work, writing essays, etc.), the material submitted must be prepared in accordance with principles for acceptable practice when referring to sources when the text, images, ideas, data, etc. of other people are used. This is done by using references or quotations for which the source is specified. It is also to be made clear whether the author has reused his or her own text, images, ideas, data, etc. from previous examinations, such as degree projects, project reports, etc. (this is sometimes known as “self-plagiarism”).

    A failure to specify such sources may be regarded as attempted deception during examination.

    Attempts to cheat

    In the event of a suspected attempt by a student to cheat during an examination, or when study performance is to be assessed as specified in Chapter 10 of the Higher Education Ordinance, the examiner is to report this to the disciplinary board of the university. Possible consequences for the student are suspension from study and a formal warning. More information is available at Cheating, deception and plagiarism.

    Linköping University has also produced a guide for teachers and students' use of generative AI in education (Dnr LiU-2023-02660). As a student, you are always expected to gain knowledge of what applies to each course (including the degree project). In general, clarity to where and how generative AI has been used is important.  

    Regulations (apply to LiU in its entirety)

    The university is a government agency whose operations are regulated by legislation and ordinances, which include the Higher Education Act and the Higher Education Ordinance. In addition to legislation and ordinances, operations are subject to several policy documents. The Linköping University rule book collects currently valid decisions of a regulatory nature taken by the university board, the vice-chancellor and faculty/department boards.

    LiU’s rule book for education at first-cycle and second-cycle levels is available at https://styrdokument.liu.se/Regelsamling/Innehall