About this course

The course deals with the practical use of a selection of mathematical methods and (continuous) functions in one and two variables that describe current issues in natural science and economics.

Determination of zeros, slopes and extrema points is described using the concept of derivation. Determination of areas and infinite summation (series) is linked to the concept of integration.

Examples of solving simple simple differential equations are presented in light of the theory of derivation and integration.

The course also provides a brief introduction to linear algebra for efficient solution of systems of equations with more than two unknown variables.

In addition to regular training in practical calculation skills, the programming language Juliawill be used for implementation and calculations on a computer.

Learning outcome

Knowledge

The candidate

  • has knowledge of the practical use of a selection of mathematical methods and continuous functions in one and two variables, especially aimed at describing current issues in the natural sciences and economics.
  • understands the concept of differentiation and how it is used to determine zeros, slopes and extrema points.
  • understands the concept of integration and its connection with determining areas and infinite summations (series).
  • knows the motivation behind simple differential equations in light of the theory of differentiation and integration.
  • has basic knowledge of linear algebra for the efficient solution of systems of equations with more than two unknown variables.

Skills

The candidate

  • has acquired good training in practical calculation skills to solve complex mathematical problems.
  • can master and use the Julia programming language to implement mathematical methods and perform calculations on a computer.
  • can apply differentiation and integration to concrete calculations, including optimization (finding max/min points), area calculations and volume.
  • can set up and solve simple differential equations.
  • can set up and solve larger systems of equations using techniques from linear algebra and Julia code.

General competence

The candidate

  • can use mathematics (through the Julia programming language) as a tool to analyze and solve practical problems from natural science and economics.
  • is able to see the connection between analytical mathematical methods (such as derivation and integration) and practical computer calculations to perform effective problem solving.

  • Learning activities

    During one week, students are given two lectures to review the most important points about each topic in the course. Lecture notes with explanations and code examples for examples and problem solving on the computer are shared with the students.

    In addition, practice tasks are given for training in methodological problem solving, mathematical understanding of the topics in the curriculum with examples of practical applications. Calculation workshops with tutors are offered several times during the week with help with problem solving.

  • Teaching support

    Guidance related to the course content is given in connection with lectures and exercise sessions. Useful information will be announced in Canvas.

    The faculty's (RealTek) oracle service for professional math-assistance will be available to the students during the teaching period.

  • Syllabus

    Will be published in Canvas.

  • Prerequisites

    Mathematics R1, S1 or 2P together with the mathematics prerequisite course (given at NMBU in August)

    In particular: Simple algebra with numbers and letters (including fractions and the three quadratic theorems), percentages, simple plane geometry (including Pythagoras' theorem), solving equations with one unknown and systems of equations with two unknowns, knowledge of linear and quadratic functions.

  • Recommended prerequisites

    See prerequisites

  • Assessment method

    3.5 hour written exam.

    School exam Karakterregel: Letter grades Hjelpemiddelkode: B2 Calculator handed out, other aids as specified

  • About use of AI

    In the organized teaching and for the exam the following applies: K1 - No use of KI

    Assignments: K2 - Specified use of AI

    The use of AI technology to support the learning process in the course is recommended.

    Use of AI must be in line with the guidelines for the use of artificial intelligence (AI) at NMBU.

    Descriptions of AI-category codes.

  • Examiner scheme

    An independent examiner will be used to approve and assess exam papers.

  • Mandatory activity

    Mandatory activities and compulsory assignments will be announced in Canvas.

    Compulsory assignments must be approved during the ongoing teaching period.

  • Notes

    Required submissions must be approved in the current academic year.

  • Teaching hours

    4 hours of lectures in the auditorium, and up to 8 hours of calculation workshop and practice groups.

  • Reduction of credits

    5 ECTS overlap with

    • ECN102
    • MATH100

  • Admission requirements

    Minimum requirements for entrance to higher education in Norway (generell studiekompetanse)