Details - Tematika

Mathematics for Computer Science I (GAINBAN-SZAMMAT1-1)

Basic data

Name and type of the study programme

Computer Science Engineering, undergraduate program

Curriculum

2022

Classes / consultation hours

2 + 2 + 0 (L+S+Labs)

Credits

5 credits

Theory – Practice

Theory: 50%, Practice: 50%

Recommended semester

Semester 1

Study mode

full-time

Prerequisites

Evaluation type

Mid-term evaluation

Course category

Compulsory

Language

English

Instructors

Responsible instructor

Dobjánné Dr. Antal Elvira Mercédesz

Responsible department

Department of Basic Sciences

Instructor(s)

Dr. Végh Attila, Dobjánné Dr. Antal Elvira Mercédesz

Checked by

Kovács Márk

Course objectives

Introduction to the basic concepts, terminology, theorems and connections of mathematical logic, set theory, combinatorics, and graph theory.

Course content

Lectures

1. Fundamentals of logic I.: propositions, equivalence. 2. Fundamentals of logic II.:predicates and quantifiers. 3. Rules of inference and proofs. 4. Sets, cartesian product of sets, set operations. 5. Correspondences, relations, functions. 6. Relations: Equivalence relations and partitions. 7. Permutations. 8. Combinatorics of finite sets. 9. Binomial theorem and multinomial theorem. 10. Mathematical induction. 11. Graphs, trees, graph coloring. 12. Basic graph algorithms.

Seminars

Problem solving relating to the lecture syllabus.

Acquired competences

Knowledge

- Knowledge of the principles and methods of natural sciences (mathematics, physics, other natural sciences) relevant to the field of IT.

Skills

- He/she uses the principles and methods of natural sciences (mathematics, physics, other natural sciences) relevant to the field of information technology in his/her engineering work for the design of information systems.

Attitude

- He/she makes decisions with full respect for the law and ethical standards in decision-making situations requiring a complex approach. - He/she makes an effort to work efficiently and to high standards.

Autonomy and responsibilities

–

Additional professional competences

- Efficient use of digital technology, knowledge of digital solutions to fulfill educational objectives

Requirements, evaluation and grading

Mid-term study requirements

Visiting lectures and seminars, taking notes, active participation in problem solving. Two 50-point written tests at times announced at the first lecture. Extra points can be earned at seminars based on class activity. Tests may be made up or substituted at the last lecture. Assessment of student performance, based on a five-grade scale: - excellent (5) if the performance is 86-100%, - good (4) if the performance is 76-85%, - satisfactory (3) if the performance is 61-75%, - pass (2) if the performance is 50-60%, - fail (1) if the performance is below 50%.

Generative AI usage

Use of GAI tools is not permitted for solving assignments. This means GAI tools cannot be used to complete formative or summative assessments, and using GAI constitutes academic misconduct. The use of AI tools for spelling and grammar checking does not fall under this prohibition.