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.

Main field

CTMAV Materials Science

Progression level in relation to degree requirements

The course is part of the main field of Materials Science and may be included in a Master of Science degree in Materials Science (120 credits).

Course contents

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

Learning outcomes

Learning outcomes

In order to pass the course, the student must be able to:

1. formulate correct algorithms for numerical computation

2. account for the statistical principles underlying machine learning

3. describe the most common types of machine learning problems

4. explain in which types of applications machine learning can be used, as well as its limitations

5. describe the scientific basis for developing and analysing learning algorithms and learning systems

Skills and abilities

In order to pass the course, the student must be able to:

6. perform stability and convergence analyses for different types of numerical schemes

7. implement numerical and machine learning algorithms in computer programs

8. apply these methods to simulate relevant problems in materials science

9. present results from project work both orally and in writing in a scientific manner

Judgement and approach

In order to pass the course, the student must be able to:

10. select methods based on a given application and assess their strengths and weaknesses

11. evaluate whether obtained computational results are consistent with the underlying assumptions

12. follow and engage with developments within the field of scientific computing and machine learning

13. discuss ethical issues that may arise in the application of machine learning-based systems

Learning activities

Lectures, seminars, laboratory work, supervised projects and independent study.

Assessment

Requirements for a Pass (UA):

• Written examination (UA), 3 credits (Learning outcomes 1–3, 7)
• Laboratory reports (Pass/Fail), 3 credits (Learning outcomes 2–7, 12)
• Presentations and participation in seminars (Pass/Fail), 1 credit (Learning outcomes 10, 12)
• Project presentation and project report (Pass/Fail), 3 credits (Learning outcomes 2, 8–11, 13)

The final course grade is determined by the grade awarded for the examination.

Course literature and other study materials

Course literature and other teaching materials

• Michael Heath, Scientific computing: An introductory survey, 2nd edition,
• Gareth James, Daniella Witten, Trevor Hastie, Rob Tibshirani,
  An introduction to statistical learning with applications in Python, Springer
• Andreas C Muller & Sarah Guido, Introduction to Machine Learning with Python, O’reilly

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).

Interim rules

If a course is no longer offered, or has undergone significant changes, the students must be offered two opportunities for re-examination based on the syllabus that applied at the time of registration, for a period of one year after the changes have been implemented. The syllabus is a translation of a Swedish source text.

Additional information

If a student has a Learning support decision, the examiner has the right to provide the student with an adapted test, or to allow the student to take the exam in a different format. The syllabus is a translation of a Swedish source text.