This course syllabus is discontinued or replaced by a new course syllabus.

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School of Science and Technology

Course Syllabus


Computer Science, Probabilistic Robotics, Second Cycle, 7.5 Credits


Course Code: DT4051 Subject Area: Field of Technology
Main Field of Study: Computer Science Credits: 7.5
    Subject Group (SCB): Computer Science
Education Cycle: Second Cycle Progression: A1N
Established: 2013-12-09 Last Approved: 2014-09-24
Valid from: Spring semester 2015 Approved by: Head of School


Aims and Objectives

General aims for second cycle education

Second-cycle courses and study programmes shall involve the acquisition of specialist knowledge, competence and skills in relation to first-cycle courses and study programmes, and in addition to the requirements for first-cycle courses and study programmes shall
- further develop the ability of students to integrate and make autonomous use of their knowledge
- develop the students' ability to deal with complex phenomena, issues and situations, and
- develop the students' potential for professional activities that demand considerable autonomy, or for research and development work.

(Higher Education Act, Chapter 1, Section 9)

Course Objectives

Knowledge and understanding
Completing this course, the student shall have good knowledge of state-of-the-art algorithms using statistical estimation techniques for solving tasks that are central to mobile robotics, and understand their mathematical background.

Applied knowledge and skills
Completing this course, the student shall be able to develop and review software that uses probabilistic techniques for robotics applications. The student shall also be able to read and understand scientific literature within the subject area of the course.

Making judgments and attitudes
Completing this course, the student will have an increased capability to understand the virtues and limitations of probabilistic approaches to solving robotics problems.


Main Content of the Course

The course has the following contents:
- Mathematical statistics: Bayes" theorem, probability distributions, generative and discriminative models
- Kalman filters
- Particle filters
- Monte Carlo optimisation
- Robot motion and sensor models
-SLAM (simultaneous localisation and mapping)
- Data association
-Random fields (Markov random fields and conditional random fields)
- Classification


Teaching Methods

Lectures and project assignment. Completing the project assignment and performing code review is mandatory, but attendance to lectures is not mandatory. In case of a small number of students, the lectures may be replaced by individual tutoring.

Students who have been admitted to and registered on a course have the right to receive tuition and/or supervision for the duration of the time period specified for the particular course to which they were accepted (see, the university's admission regulations (in Swedish)). After that, the right to receive tuition and/or supervision expires.


Examination Methods

Theory, 5 Credits. (Code: 0200)
Written examination. In case of a small number of students, the written examination may be replaced by oral examination.
Practice, 2.5 Credits. (Code: 0300)
Project work and code review.

For further information, see the university's local examination regulations (in Swedish).


Grades

According to the Higher Education Ordinance, Chapter 6, Section 18, a grade is to be awarded on the completion of a course, unless otherwise prescribed by the university. The university may prescribe which grading system shall apply. The grade is to be determined by a teacher specifically appointed by the university (an examiner).

According to regulations on grading systems for first- and second-cycle education (vice-chancellor's decision 2010-10-19, reg. no. CF 12-540/2010), one of the following grades is to be used: fail, pass, or pass with distinction. The vice-chancellor or a person appointed by the vice-chancellor may decide on exceptions from this provision for a specific course, if there are special reasons.

Grades used on course are Fail (U), Pass (G) or Pass with Distinction (VG).

Theory
Grades used are Fail (U), Pass (G) or Pass with Distinction (VG).
Practice
Grades used are Fail (U) or Pass (G).


The final grade of the course is given by the grade of the theoretical part, provided that the practical part is approved.


For further information, see the university's local examination regulations (in Swedish).


Specific entry requirements

First-cycle degree of 180 credits, with Computer Science as the main field of study, and at least 15 credits in mathematics (analysis and algebra). The applicant must also have qualifications corresponding to the course "English B" or "English 6" from the Swedish Upper Secondary School.
OR
First-cycle degree of 180 credits, and at least 30 credits in mathematics (analysis and algebra), as well as at least 15 credits in Computer Science or Informatics (which includes programming). The applicant must also have qualifications corresponding to the course "English B" or "English 6" from the Swedish Upper Secondary School.

For further information, see the university's admission regulations (in Swedish).


Transfer of Credits for Previous Studies

Students who have previously completed higher education or other activities are, in accordance with the Higher Education Ordinance, entitled to have these credited towards the current programme, providing that the previous studies or activities meet certain criteria.


For further information, see the university's local credit transfer regulations (in Swedish).


Other Provisions

The course is given in English.


Reading List and Other Teaching Materials

Required Reading

Thrun Sebastian, Burgard Wolfram, Fox Dieter (2005)
Probabilistic Robotics
MIT Press, 647 pages


Additions and Comments on the Reading List

Ytterligare material kan utdelas under kursens gång.


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