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Mathematical Methods for Computer Engineering (MATLC0124), 5 op
Basic information
| Course name: | Mathematical Methods for Computer Engineering Mathematical Methods for Computer Engineering |
| Course Winha code: | MATLC0124 |
| Kurre acronym: | MathMeth |
| Credits: | 5 |
| Type and level of course: | Professional studies |
| Year of study, semester or study period: | 3.year |
| Implementation: | Spring semester, Autumn semester, 1.period, 2.period |
| Semester: | 0708 |
| Language of tuition: | English |
| Teacher: | Jaakko Pitkänen |
| Final assessment: | Grading scale (0-5) |
Descriptions
Prerequisites
MATLC0020 Basic Course of Mathematics, MATLC0128 Integral Calculus and laplace Transforms, MATLC0022 Series, Fourier- and Z-Transforms
Course contents (core content level)
Probability, binomial, Poisson and normal distributions. Mean and variance, statistical testing of means. Mathematical modeling, regression models. Discrete mathematics, Euclidean algorithm, congruences and graph theory. Some optimization problems.
Course contents (additional)
Conditional probability. Reliability, exponential distribution. Median and quartiles, statistical testing of variance and distribution. Variance analysis, nonlinear models. Diophantine equations, RSA algorithm. Algebraic structures.
Core content level learning outcomes (knowledge and understanding)
After completion of this course the student understands the meaning of probability and statistical testing and the areas of application for different distributions. The student understands mathematical models. The student is familiar with the applications of discrete mathematics to coding theory and optimization.
Core content level learning outcomes (skills)
After completion of this course the student is able to choose the correct distribution and apply it. The student can do simple statistical testing and fit a model to the data. The student is able to use simple coding and optimization algorithms.
Recommended reading
Croft ? Davison ? Hargreaves: Engineering Mathematics
Kreyszig: Advanced Engineering Mathematics
Teaching and learning strategies
Lectures and assignments
laboratory assignments
examinations
homework and self-study
Teaching methods and student workload
Exam
Lectures and assignments
Self-study
Laboratory assignments
Assessment weighting and grading
Examinations (40 % of maximal points) and computer assignments