About this course

This subject provides knowledge about

  • mathematical foundations for the analysis of algorithms
  • some essential algorithms and data structures
  • methods for the performance analysis of algorithms
  • the proof of correctness of algorithms
  • about key problem-solving strategies
  • the application of algorithms in data science.

Learning outcome

Upon completion of this course, you will be able to analyze the performance and correctness of algorithms, evaluate which algorithms and data structures are suitable for given purposes, and knowledge essential problem-solving strategies. You can also apply these techniques within data science.

  • Learning activities

    Course material is presented and discussed in lectures. In tutoring sessions, you perform analyses under supervision. You will learn to present and discuss theoretical analysis of and practical experiments on the performance of algorithms through reports.

  • Teaching support

    Guidance during tutoring sessions.

  • Syllabus

    Will be announced at the beginning of the course.

  • Prerequisites

    INF120 and INF201 comparable programming competence, preferably in Python.

    MATH111/121 or a comparable calculus course.

    DAT110/STAT100 or a comparable statistics course.

  • Assessment method

    Evaluation: 3,5 hour written exam. A-F.

    Written exam

    Karakterregel: Letter grades

    Hjelpemiddel: B2 Provided calculator, specified aids: the textbook Algorithms Unlocked by Thomas Cormen

  • About use of AI

    Written exam: K1

    Mandatory exercises: As it will not be realistic to reliably check for use of AI in hand-ins, code K3 applies. At the same time, students are most strongly alerted to the fact that the purpose of mandatory activity is preparation for the exam where no AI will be available.

    Adjustments are possible depending on technological development.

    The use of AI must comply with the guidelines for the use of artificial intelligence (AI) at NMBU.

    Descriptions of AI-category codes.

  • Examiner scheme

    The co-examiner approves the final exam as well as evaluation guidelines. The co-examiner reviews the main examiner's examination results by correcting a random sample of candidate's exams as a calibration according to the faculty's guidelines for examination markings.

  • Mandatory activity

    Mandatory exercises. Details will be given at the beginning of the course.

  • Teaching hours

    26 hours of lectures, 14 hours of exercises

  • Reduction of credits

    10 credits with INF220 taught before 2010 and with INF221 taught before 2025.

  • Admission requirements

    Special requirements in Science.