Semester: 6
Elective
ECTS: 6
Hours per week: 4
Professor: T.B.D.
Teaching style: Lectures and exercises, Face to face
Grading: Theory (exams, short test) (50%), Laboratory (50%)
Activity | Workload |
---|---|
Lectures | 26 |
Tutorial Exercises | 0 |
Laboratory Exercises | 26 |
Computational Exercises | 0 |
Personal study | 98 |
Course total | 150 |
The specific course is an introduction to basic concepts of Artificial Neural Networks (ANNs) and Machine Learning (ML) techniques. As the time passes, the courses deepen in more specialized knowledge and concepts that require ability and skills development from the students, both understanding and use of new technologies.
This course is also a technological analysis on issues, problems and applications related to modeling techniques based on artificial intelligence.
The aim of the course is the understanding of the basic elements of ANNs and ML in issues related to applications of modeling and forecasting for Engineers.
Upon completion of the course, students will be able to:
More specifically, students will be able to:
Theoretical knowledge concerning ANNs and ML modeling techniques
Search, analysis and synthesis of data and information using and applying the required technologies
Practical ability of applications concerning ANNs & ML
Ability to evaluate the accuracy and the efficiency of developed ANNs & ML models
Decision making
Individual project
Teamwork
Theory
The core modules of the course include:
Laboratory
The workshop includes the following laboratory exercises:
Russell, R. (2018). Neural Networks. Easy Guide to Artificial Neural Networks. CreateSpace Independent Publishing Platform. ISBN-10: 1718898428, ISBN-13: 978-1718898424
Beale, R., & Jackson, T. (1990). Neural Computing: Αn Ιntroduction. Ν.Υ.: Adam Hilger.
Snehashish Chakraverty and Sumit Kumar Jeswal, (2021). Applied Artificial Neural Network Methods for Engineers and Scientists. World Scientific Publishing Co. Pte. Ltd. https://doi.org/10.1142/12097
The specific course is an introduction to basic concepts of Artificial Neural Networks (ANNs) and Machine Learning (ML) techniques. As the time passes, the courses deepen in more specialized knowledge and concepts that require ability and skills development from the students, both understanding and use of new technologies.
This course is also a technological analysis on issues, problems and applications related to modeling techniques based on artificial intelligence.
The aim of the course is the understanding of the basic elements of ANNs and ML in issues related to applications of modeling and forecasting for Engineers.
Upon completion of the course, students will be able to:
More specifically, students will be able to:
Theoretical knowledge concerning ANNs and ML modeling techniques
Search, analysis and synthesis of data and information using and applying the required technologies
Practical ability of applications concerning ANNs & ML
Ability to evaluate the accuracy and the efficiency of developed ANNs & ML models
Decision making
Individual project
Teamwork
Theory
The core modules of the course include:
Laboratory
The workshop includes the following laboratory exercises:
Russell, R. (2018). Neural Networks. Easy Guide to Artificial Neural Networks. CreateSpace Independent Publishing Platform. ISBN-10: 1718898428, ISBN-13: 978-1718898424
Beale, R., & Jackson, T. (1990). Neural Computing: Αn Ιntroduction. Ν.Υ.: Adam Hilger.
Snehashish Chakraverty and Sumit Kumar Jeswal, (2021). Applied Artificial Neural Network Methods for Engineers and Scientists. World Scientific Publishing Co. Pte. Ltd. https://doi.org/10.1142/12097