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Blekinge Institute of Technology
Department of Mathematics and Natural Science

Revision: 1
Reg.no:


Course syllabus

Discrete Mathematics and Algorithms

Discrete Mathematics and Algorithms

7.5 credits (7,5 högskolepoäng)

Course code: MA1517
Main field of study: Mathematics
Disciplinary domain: Natural sciences
Education level: First-cycle
Specialization: G1N - First cycle, has only upper-secondary level entry requirements

Language of instruction: English
Applies from: 2026-06-10
Approved: 2026-06-10

1. Decision

This course is established by Dean 2026-04-29. The course syllabus is approved by Head of Department of Mathematics and Natural Science 2026-06-10 and applies from 2026-06-10.

2. Prior knowledge

2.1 Entry requirements

General entry requirements + Mathematics 3b or 3c. Or Mathematics - Advanced level 1b alternatively Mathematics - Advanced level 1c

3. Objective and content

3.1 Objective

The purpose of the course is to introduce mathematical concepts, methods, algorithms, and problem formulations within discrete mathematics and to provide a foundation for further studies in mathematics and computer science.

3.2 Content

  • Set Theory
    • Basic concepts and operations.
    • Classic sets.
  • Arithmetic
    • Division algorithm, divisibility, and prime numbers.
    • Euclidean algorithm.
    • Linear Diophantine equations.
    • Number bases.
    • Modular arithmetic and linear congruences.
  • Recursion and Induction
    • Sequences.
    • Sums and products.
  • Combinatorics and Probability
    • Addition and multiplication principles.
    • Selections with/without replacement and with/without regard to order.
    • Binomial theorem.
    • Conditional probability.
  • Logic
    • Propositional and predicate logic.
  • Graph Theory
    • Basic definitions.
    • Euler circuits and Hamiltonian cycles.
    • Representation of graphs.
    • Trees.
  • Relations and Functions
    • Partial orders
    • Equivalence relations and partitions.
  • Finite Automata
    • Formal languages.

4. Learning outcomes

The following learning outcomes are examined in the course:

4.1. Knowledge and understanding

On completion of the course, the student will be able to:

  • Demonstrate understanding of concepts and theorems contained in the course material.

4.2. Competence and skills

On completion of the course, the student will be able to:

  • Solve calculation tasks and problems within the course material.

4.3. Judgement and approach

On completion of the course, the student will be able to:

  • Show that they have experienced the usefulness and limitations of treated algorithms.

5. Learning activities

Instruction is given in the form of lectures and exercises.

6. Assessment and grading

Modes of examinations of the course

Code Module Credit Grade
2705 On-Campus Examination 7.5 credits AF

The course will be graded A Excellent, B Very good, C Good, D Satisfactory, E Sufficient, FX Failed result, a little more work required, F Fail.

The examiner may carry out oral follow-up of written examinations.

The information before the start of the course states the assessment criteria and make explicit in which modes of examination that the learning outcomes are assessed.

An examiner can, after consulting the Disability Advisor at BTH, decide on a customized examination form for a student with a long-term disability to be provided with an examination equivalent to one given to a student who is not disabled.

7. Course evaluation

The course evaluation should be carried out in line with BTH:s course evaluation template and process.

8. Restrictions regarding degree

The course can form part of a degree but not together with another course the content of which completely or partly corresponds with the contents of this course.

9. Course literature and other materials of instruction

Required course literature

Eriksson, K and Gavel, H, (2025 or later), *Discrete mathematics and discrete models,*Studentlitteratur AB, ISBN: 9789144202006.

Course material on the learning platform.

Other learning resources

The instructor is a central learning resource in the course. During lecture, a range of information is conveyed regarding, for example, solution strategies, mathematical conventions, and course level that cannot be expected to be acquired any other way than by participating in the lecture hall.