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This course is offered as part of programme:
Course content
Part 1
- Accuracy and convergence of numerical approximation
- Linear and non-linear systems of equations
- Least squares methods and data fitting
- Optimisation
- Numerical differentiation and integration
- Runge–Kutta methods for ordinary differential equations
Part 2
- Multidimensional random variables and their distributions
- Regression analysis
- Dimensionality reduction
- Clustering techniques
- Supervised and unsupervised learning
- Deep learning and neural networks
- Practical applications including ethical considerations
Entry requirements
1\. Bachelor's degree of at least 180 credits within material engineering, machine engineering, physics, chemistry or the equivalent
2\. At least 22.5 credits of Mathematics.
3\. English 6. Or: English level 2.
Course literature
Current literature list is available in the syllabus for the course
Course evaluation
Malmö University provides students who participate in, or who have completed a course, with the opportunity to express their opinions and describe their experiences of the course by completing a course evaluation administered by the University. The University will compile and summarise the results of course evaluations. The University will also inform participants of the results and any decisions relating to measures taken in response to the course evaluations. The results will be made available to the students (HF 1:14).
