EPN-V2

PENG9610 Evidence-based engineering Course description

Course name in Norwegian
Evidence-based engineering
Study programme
PhD Programme in Engineering Science
Weight
5.0 ECTS
Year of study
2022/2023
Programme description
Course history

Introduction

The following required coursework must be approved before the student can take the exam:

Two mandatory group assignments consisting of technical tasks, summarized in reports (about 10 pages each).

Recommended preliminary courses

Master's degree in engineering science or related fields.

Required preliminary courses

The exam consists of three parts:

  1. Oral presentation of 15 minutes (20% of the final grade), individual or in a group of two
  2. Written evaluation of another student presentation, 500-1000 words (10% of the final grade), individual or in a group of two
  3. Final project report between 6000 and 11,000 words (70% of the final grade), individual or in a group of two.

All three parts of the exam must be passed in order to pass the course.

The oral examination cannot be appealed.

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for registering for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

Learning outcomes

All aids are permitted, provided the rules for plagiarism and source referencing are complied with.

Content

The topics covered in this course aims at giving the students the necessary skills to be evidence-based and includes lectures on:

  • The basics of evidence-based engineering
  • Empirical methods and their use in engineering disciplines
  • How to critically evaluate and give rational argumentations
  • How to collect, evaluate and use practice-based experience
  • How to collect, evaluate and use research-based evidence
  • How to conduct empirical studies
  • How to aggregate evidence
  • How to design good judgment and decision-making processes

 

To illustrate the topics and exemplify the use of evidence-based decision processes, selected engineering topics, such as software development methods, cost estimation, project management and workplace organization, will be used as cases. The course contains mandatory deliveries, exercises and student presentations.

Teaching and learning methods

Grade scale A-F.

Course requirements

Two internal examiners. External examiner is used periodically.

Assessment

Associate Professor Henrik Lieng

Permitted exam materials and equipment

Practical experience with deep machine learning. Knowledge of computer graphics and image processing is preferable, but not strictly required.

Grading scale

  • Convolutional neural networks in 3D
  • Deep learning for point clouds
  • Convolutional neural networks on graphs
  • Neural radiance fields
  • Joint embedding for images and 3D data

Examiners

Two examiners. External examiner is used periodically.