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Machine learning and reasoning with built environment data (5 ECTS)

Code: TX00FE98-3003

General information


Enrollment
02.12.2024 - 06.01.2025
Registration for the implementation has ended.
Timing
13.01.2025 - 25.05.2025
Implementation is running.
Number of ECTS credits allocated
5 ECTS
Mode of delivery
On-campus
Unit
(2019-2024) School of Real Estate and Construction
Teaching languages
English
Seats
0 - 20
Degree programmes
Master's Degree Programme in Computing in Construction
Teachers
Yan Peng
Seppo Törmä
Teacher in charge
Seppo Törmä
Groups
T2424S6
Master's Degree Programme in Computing in Construction, ylempi
Course
TX00FE98

Implementation has 18 reservations. Total duration of reservations is 63 h 0 min.

Time Topic Location
Mon 17.03.2025 time 08:00 - 12:00
(4 h 0 min)
Machine learning and reasoning with built environment data TX00FE98-3003
MPA3011 Digitila
Thu 20.03.2025 time 13:00 - 16:00
(3 h 0 min)
Machine learning and reasoning with built environment data TX00FE98-3003
MPA4017 Digitila
Mon 24.03.2025 time 08:00 - 12:00
(4 h 0 min)
Machine learning and reasoning with built environment data TX00FE98-3003
MPA3011 Digitila
Thu 27.03.2025 time 13:00 - 16:00
(3 h 0 min)
Machine learning and reasoning with built environment data TX00FE98-3003
MPA4017 Digitila
Mon 31.03.2025 time 08:00 - 12:00
(4 h 0 min)
Machine learning and reasoning with built environment data TX00FE98-3003
MPA3011 Digitila
Thu 03.04.2025 time 13:00 - 16:00
(3 h 0 min)
Machine learning and reasoning with built environment data TX00FE98-3003
MPA4017 Digitila
Mon 07.04.2025 time 08:00 - 12:00
(4 h 0 min)
Machine learning and reasoning with built environment data TX00FE98-3003
MPA3011 Digitila
Thu 10.04.2025 time 13:00 - 16:00
(3 h 0 min)
Machine learning and reasoning with built environment data TX00FE98-3003
MPA4017 Digitila
Mon 14.04.2025 time 08:00 - 12:00
(4 h 0 min)
Machine learning and reasoning with built environment data TX00FE98-3003
MPA3011 Digitila
Thu 17.04.2025 time 13:00 - 16:00
(3 h 0 min)
Machine learning and reasoning with built environment data TX00FE98-3003
MPA4017 Digitila
Thu 24.04.2025 time 13:00 - 16:00
(3 h 0 min)
Machine learning and reasoning with built environment data TX00FE98-3003
MPA4017 Digitila
Mon 28.04.2025 time 08:00 - 12:00
(4 h 0 min)
Machine learning and reasoning with built environment data TX00FE98-3003
MPA3011 Digitila
Mon 05.05.2025 time 08:00 - 12:00
(4 h 0 min)
Machine learning and reasoning with built environment data TX00FE98-3003
MPA3011 Digitila
Thu 08.05.2025 time 13:00 - 16:00
(3 h 0 min)
Machine learning and reasoning with built environment data TX00FE98-3003
MPA4017 Digitila
Mon 12.05.2025 time 08:00 - 12:00
(4 h 0 min)
Machine learning and reasoning with built environment data TX00FE98-3003
MPA3011 Digitila
Thu 15.05.2025 time 13:00 - 16:00
(3 h 0 min)
Machine learning and reasoning with built environment data TX00FE98-3003
MPA4017 Digitila
Mon 19.05.2025 time 08:00 - 12:00
(4 h 0 min)
Machine learning and reasoning with built environment data TX00FE98-3003
MPA3011 Digitila
Thu 22.05.2025 time 13:00 - 16:00
(3 h 0 min)
Machine learning and reasoning with built environment data TX00FE98-3003
MPA4017 Digitila
Changes to reservations may be possible.

Objective

The student can identify and explain the basic concepts of validating data and deriving new data from existing data through data analysis, machine learning, machine inference, rule-based systems, and artificial intelligence in general. The student understands the benefits and prerequisites of the technologies and the potential use scenarios in construction domain. The student knows and can apply a set of tools for data validation and data derivation for data in construction domain. The student can programmatically integrate these technologies to broader software solutions in the construction domain.

Content

- Introduction to artificial intelligence, learning and reasoning
- Application areas of artificial intelligence technologies in the construction domain
- Machine learning approaches, including neural networks and deep learning
- Semantic models
- Logical and ontology-based reasoning
- Knowledge graphs in construction
- Rule-based reasoning and validation
- Rule-based checking of BIM models

Location and time

Metropolia Myllypuro campus, 4th period 2025

Teaching methods

Lectures
Online courses
Assignment
Exam

Evaluation scale

0-5

Assessment criteria, satisfactory (1)

The student has achieved the minimal objectives of the course. The student can identify and explain the concepts and approaches related to data analysis, machine learning, reasoning, and rule-based systems and their potential use scenarios in the construction domain. The student knows and is familiar with some software tools in the area. The student has completed the required learning exercises in minimum requirement level. The competencies acquired form the basis for the student to build his/her knowledge in machine learning and reasoning in construction domain, eventually enabling a job position in applying these tools.

Assessment criteria, good (3)

The student has achieved the objectives of the course well, even though the knowledge and skills need improvement on some areas. The student can identify and explain the concepts and approaches related to data analysis, machine learning, reasoning, and rule-based systems and their potential use scenarios in the construction domain. The student knows and can apply software tools in the area. The student has completed the required learning exercises in good or satisfactory level. The student is able to create software solutions incorporating machine learning and reasoning functionalities. The student has the capability to apply the knowledge in further studies and in software development work related machine learning and reasoning.

Assessment criteria, excellent (5)

The student has achieved the objectives of the course with excellence. The student can identify and explain the concepts and approaches related to data analysis, machine learning, reasoning, and rule-based systems and their potential use scenarios in the construction domain. The student knows and can apply multiple software tools in the area. The student has completed the required learning exercises in excellent or good level. The student is able to integrate well-placed machine learning and reasoning functionalities in complex software solutions in a justified manner. The student has an excellent basis to apply the knowledge in further studies and in software development related to machine learning and reasoning.

Qualifications

TX00FE95
TX00FE96

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