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Course Catalog 2014-2015
ASE-8016 Advanced Topics in Automation Science and Engineering , 1-10 cr |
Additional information
Suitable for postgraduate studies
Person responsible
Jukka Lekkala
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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Requirements
Seminar or exam.
Completion parts must belong to the same implementation
Learning Outcomes
The student will develop his/her scientific competence by deepening his/her knowledge in postgraduate topics in automation science and engineering. The student will develop his/her oral presentation and writing skills.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Reading up on postgraduate topics in automation science and engineering either independently or in seminars. | Oral presentation. Scientific reporting. |
Prerequisite relations (Requires logging in to POP)
Correspondence of content
| Course | Corresponds course | Description |
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More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |
| Intelligent vehicles: a MOOC with TUT supervision. MOOC (Massive open online course) provided by Udacity https://www.udacity.com/course/cs373 | |||
| Bayesian data analysis [2 credits]. This short course is an introduction to the use of Matlab/JAGS software for statistical modelling of engineering systems data. The textbook is Kelly & Smith (2011): Bayesian Inference for Probabilistic Risk Assessment, A Practitioner's Guidebook (available on-line). Topics include: inference and prediction for lifetime/duration and count data, model checking, regression, missing data. Pass requirement: presentation of computer modelling homework solutions at 4 exercise sessions. Grade is pass/fail. | |||
| Introduction to Discrete-Time Control. The compulsory sessions are the exam(s) and four Matlab sessions of 120 minutes each. |