Muutokset laskennallisen tekniikan opetussuunnitelmaan 2020-2021 Muutokset laskennallisen tekniikan opetussuunnitelmaan 2020-2021

Tällä sivulla ilmoitetaan muutoksista voimassaolevaan opinto-oppaaseen. Nämä muutokset on hyväksynyt joko koulutusohjelmasta vastaava henkilö tai akateeminen neuvosto.

This page contains the changes made to the current year's study guide. These changes are accepted by the head of the degree programme or the academic council meeting.

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Opintoluonnokset/Study Drafts in 2020-2021

 

Tekniikan kandidaatin tutkinnon vapaasti valittava työharjoittelu, 1-6 op, harjoittelun tarkastajana toimii Matylda Jablonska-Sabuka
Työharjoitteluohjeet Unissa

Work Internship in Master's Degree/2‐10 op, internship coordinators by specialisation
Instructions at Uni-portal

 

Project Work in Applied Mathematics, Computer Vision and Technical Physics, 10 - 30 op

M.Sc. (Tech.) 1-2, 1-4 periodi

Enrolment by email to Matylda Jablonska‐Sabuka, Bernardo Barbiellini or Arto Kaarna

Aims: The student obtains practical skills on research methods and practices and obtains advanced knowledge in a specific application area. The student gains experience in project work, team work skills, self-management and work discipline.

Contents: A specific project completed in one of the research areas in Applied Mathematics, Computer Vision or Technical Physics. The project is planned together with the supervisor(s) and the work consists of a literature survey, modeling, implementations, data collection, analysis of results, and reporting. The course may contain lectures and seminars. The project may also be planned together with industry and partly carried out in the environment of the company.

Teaching methods: Research work 170-500 h, independent study 50-200 h, report preparation 50-100 h. The granted ECTS credits will be defined according to the actual working hours.

Assessment scale and assessment methods: 0-5 or pass/fail, depending on the work performance and project report.

 

Advanced Methods in Mathematics, Computing and Physics, 3 - 6 op

M.Sc. (Tech.) 2, 1-4 period

Enrolment by email to Matylda Jablonska‐Sabuka, Bernardo Barbiellini or Arto Kaarna

Aims: The student is able to employ theoretical and operational skills in some specific area of applied mathematics, computing, and technical physics. The student is able to select, apply, and analyze methods to modeling problems in mathematics, science and engineering. Entrepreneurial learning methods are applied.

Contents: The course consists of literature review, working on assignments and completing practical projects. Materials will be chosen and agreed individually according to the focus of the study module, students’ interests, and research in the laboratories. The course with the same title can be included in the study programme twice when two distinct areas are covered in the projects.

Teaching methods: Self-study of learning materials, exercises, assignments and reporting, seminar presentation, total 80-160 h.

Assessment scale and assessment methods: Pass/Fail, report and seminar presentation 100 %.

 

Tohtoriopinnot/Doctoral Studies:

 

Sovelletun matematiikan jatko-opintokurssi, 5 - 7 op

Tohtoriopinnot, pidetään tarvittaessa

Ilmoittautuminen sähköpostilla professori Heikki Haariolle.

Tavoitteet: Perehdytään johonkin sovelletun matematiikan erikoisalueeseen.

Sisältö: Vaihtuva-aiheinen kurssi laitoksen omien ja vierailevien tutkijoiden erikoisaloilta, esim. DY/ODY-mallinnuksen, data-analyysin, signaali-analyysin, stokastiikan, numeriikan ja matematiikan kehittyvien sovellusten parista. Opintoluonnoksen voi suorittaa ja sisällyttää jatkotutkintoon useammin kuin kerran.

Suoritustavat: Luentoja ja seminaariesityksiä. Seminaariesitelmä/raportti/harjoitustyö/kirjatentti. Ajankohta sovitaan

professorin kanssa.

Arviointiskaala ja arviointimenetelmät: Hyväksytty-hylätty

 

Advanced Kalman Filtering, 5 - 12 op

Doctoral studies, will be organized on request

Enrolment by email to professor Heikki Haario

 Aims: Students get a deeper understanding of several Kalman filtering methods, and learn to apply Kalman filtering to solve practical problems.

Contents: Basic and extended Kalman filters, optimization formulations, ensemble and particle filters. Applications to engineering problems.

Teaching methods: Lectures and seminar presentations. Practical assignments.

Assessment scale and assessment methods: Pass-fail. Seminar presentations 30 %, assignments 70 %.

 

Statistical Methods for Inverse Problems, 5 - 12 op

Doctoral studies, fall semester

Enrolment by email to Assoc. prof. Lassi Roininen

Aims: Students get a deeper understanding of statistical methods for inverse problems and learn to apply the methods to practical problems.

Contents: Bayesian approach to inverse problems. Various Markov chain Monte Carlo methods, together with different prior and likelihood formulations. Applications to science and industrial engineering problems.

Teaching methods: Lectures and seminar presentations. Practical assignments.

Assessment scale and assessment methods: Pass-fail. Seminar presentations 30 %, assignments 70 %.

 

Research Seminar on Scientific Computing for Doctoral Students, 2 - 6 op

Doctoral studies, 1-4 period

Enrolment by email to Ashvinkumar Chaudhari

Aims: To give doctoral students skills to present their research work and to disseminate their results to an audience as well as skills to carry out reviews of other researchers’ work and participate in academic debates. More general objectives of the seminar are to share concrete methodological advancements and new results among doctoral students, and to distribute information on research work carried out at LUT aiming at doctoral theses also to staff members who are interested in to attend the seminar audience.

Contents: The seminar presentations deal with a wide range of topics of scientific computing and other research methodology development carried out in all LUT schools.

Teaching methods:

Total 4 seminars per academic year. 1 seminar (2 h) per period. The credits distribution is as following:

6 ECTS: giving 3 presentations and acting as an opponent to 3 presentations as well as being actively present in the seminar 3 times as an audience (totally 9 seminars)

4 ECTS: giving 2 presentations and acting as an opponent to 2 presentations as well as being actively present in the seminar 2 times as an audience (totally 6 seminars)

2 ECTS: giving 1 presentation and acting as an opponent to 1 presentation as well as being actively present in the seminar as an audience (totally 3 seminars)

Assessment scale and assessment methods: Pass/Fail

 

Advanced Topics in Computer Vision and Pattern Recognition, 2 - 6 op

Doctoral studies, will be organized on request

Enrolment by email to professor Lasse Lensu

Aims: To support doctoral studies by introducing recent research issues in computer science, specifically in the areas of computer vision and pattern recognition.

Contents: The topic changes annually.

Teaching methods:

2 ECTS cr: Lectures, seminar presentations, discussions, independent study and exercises. Oral presentations, written seminar reports, active participation in the course. Total workload 52 h.

6 ECTS cr: Lectures, seminar presentations, discussions, independent study and exercises. Oral presentations, written seminar reports, active participation in the course and a practical assignment. Total workload 156 h.

Assessment scale and assessment methods:

2 ECTS cr: Pass/Fail: Oral presentations and written reports 50 %, active participation (lectures, seminars, discussions) 50 %.

6 ECTS cr: Pass/Fail: Oral presentations and written reports 20 %, active participation (lectures, seminars, discussions) 20 % and practical assignment 60 %.