This page includes information about aim and learning outcomes, teachers, literature, examination, examination procedures, old exams and course evaluation. The program for all teaching sessions can be found on a separate page (Course syllabus).

Aim and learning outcomes

The aim and learning outcomes of the course can be found in the course plan.

Changes compared to the last occasion

Some adjustments of the exercises.

Teachers

Examiner: Petter Mostad

Lecturer: Petter Mostad

Teaching assistants: None

Lab supervisor: Petter Mostad

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Course literature

Our required reading will be selected from three different textbooks, all available online, and some extra material described in lecture notes. We will use:

(B) Bishop: Pattern Recognition And Machine Learning, available here. The parts we cover will be specified in the syllabus.
(A) Albert: Bayesian Computation with R. Available as e-book. The parts we cover will be specified in the syllabus.
(RC) Robert and Casella: Introducing Monte Carlo Methods with R. Available as e-book. Also available: Solutions to odd-numbered exercises, and errata. The parts we cover will be specified in the syllabus.
Lecture notes for the course lectures will be made available on the syllabus page. They will contain some additional material not covered in the texts above.

English-Swedish mathematical dictionary.

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Examination

To pass the course you need to

have approved answers to the assignments.
pass the final written exam. No aids are allowed during the exam. Your grade for the course is based on the grade from the written exam. The exam will contain questions asking you to describe/explain/prove theory, and questions asking you to apply theory to specific situations, to obtain specific equations or computational algorithms.

The written exam and the assignments are registered separately in Ladok. This means that you can pass each in different years. If you need to pass just one of these components, please re-register for the whole course.

The final written exam will give a maximal score of 30 points.
The Chalmers grading scale is 12-17.5: 3; 18-23.5: 4; 24-30: 5.
The GU grading scale is 12-21.5: G; 22-30: VG.

The dates, times and places for the exams are in the student portal.

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Examination procedures

You can read about what rules apply to exams at Chalmers here.

Before the exam, it is important that you sign up for the examination. You sign up through Ladok.

At the exam, you should be able to show valid identification.

After the exam has been graded, you can see your results in Ladok.

At the annual (regular) examination:
When it is practical, a separate review is arranged. The date of the review will be announced here on the course homepage. Anyone who can not participate in the review may thereafter retrieve and review their exam at the Mathematical Sciences Student office. Check that you have the right grades and score. Any complaints about the marking must be submitted in writing at the office, where there is a form to fill out.

At re-examination:
Exams are reviewed and retrieved at the Mathematical Sciences Student office. Check that you have the right grades and score. Any complaints about the marking must be submitted in writing at the office, where there is a form to fill out.

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Course evaluation

At the beginning of the course, at least two student representatives should have been appointed to carry out the course evaluation together with the teachers. The evaluation takes place through conversations between teachers and student representatives during the course and at a meeting after the end of the course when the survey result is discussed and a report is written.

Guidelines for Course evaluation in Chalmers student portal.