| Code - Course Title |
MKM611 - Artificial Intelligence in Mechanical Engineering Applications |
| Course Type |
Elective Courses |
| Language of Instruction |
Türkçe |
| Laboratory + Practice |
3+0 |
| ECTS |
7.5 |
| Course Instructor(s) |
PROFESÖR DOKTOR OĞUZ ÇOLAK |
| Mode of Delivery |
Face to face |
| Prerequisites |
Basic Programming (Python/MATLAB), engineering mathematics |
| Courses Recomended |
|
| Required or Recommended Resources |
1-Goodfellow, I., Bengio, Y., & Courville, A. (2016).Deep learning. MIT Press.ISBN: 97802620356132-Bishop, C. M. (2006).Pattern recognition and machine learning. Springer.ISBN: 97803873107323-Hastie, T., Tibshirani, R., & Friedman, J. (2009).The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.https://doi.org/10.1007/978-0-387-84858-74-Brunton, S. L., & Kutz, J. N. (2019).Data-driven science and engineering: Machine learning, dynamical systems, and control. Cambridge University Press.https://doi.org/10.1017/97811083806905-Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019).Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.Journal of Computational Physics, 378, 686–707.https://doi.org/10.1016/j.jcp.2018.10.0456-Tao, F., Zhang, M., Liu, Y., & Nee, A. Y. C. (2019).Digital twin driven product design, manufacturing and service with big data.The International Journal of Advanced Manufacturing Technology, 94, 3563–3576.https://doi.org/10.1007/s00170-017-0233-17-Kusiak, A. (2018).Smart manufacturing.International Journal of Production Research, 56(1–2), 508–517.https://doi.org/10.1080/00207543.2017.13516448-Wuest, T., Weimer, D., Irgens, C., & Thoben, K. D. (2016).Machine learning in manufacturing: Advantages, challenges, and applications.Production & Manufacturing Research, 4(1), 23–45.https://doi.org/10.1080/21693277.2016.11925179-Jones, D. R., Schonlau, M., & Welch, W. J. (1998).Efficient global optimization of expensive black-box functions.Journal of Global Optimization, 13, 455–492.https://doi.org/10.1023/A:1008306431147 |
| Recommended Reading List |
1-Goodfellow, I., Bengio, Y., & Courville, A. (2016).Deep learning. MIT Press.ISBN: 97802620356132-Bishop, C. M. (2006).Pattern recognition and machine learning. Springer.ISBN: 97803873107323-Hastie, T., Tibshirani, R., & Friedman, J. (2009).The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.https://doi.org/10.1007/978-0-387-84858-74-Brunton, S. L., & Kutz, J. N. (2019).Data-driven science and engineering: Machine learning, dynamical systems, and control. Cambridge University Press.https://doi.org/10.1017/97811083806905-Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019).Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.Journal of Computational Physics, 378, 686–707.https://doi.org/10.1016/j.jcp.2018.10.0456-Tao, F., Zhang, M., Liu, Y., & Nee, A. Y. C. (2019).Digital twin driven product design, manufacturing and service with big data.The International Journal of Advanced Manufacturing Technology, 94, 3563–3576.https://doi.org/10.1007/s00170-017-0233-17-Kusiak, A. (2018).Smart manufacturing.International Journal of Production Research, 56(1–2), 508–517.https://doi.org/10.1080/00207543.2017.13516448-Wuest, T., Weimer, D., Irgens, C., & Thoben, K. D. (2016).Machine learning in manufacturing: Advantages, challenges, and applications.Production & Manufacturing Research, 4(1), 23–45.https://doi.org/10.1080/21693277.2016.11925179-Jones, D. R., Schonlau, M., & Welch, W. J. (1998).Efficient global optimization of expensive black-box functions.Journal of Global Optimization, 13, 455–492.https://doi.org/10.1023/A:1008306431147 |
| Assessment methods and criteria |
Project Presentation:@Final Project Report: @ Participation & Discussions: 20% |
| Work Placement |
No |
| Sustainability Development Goals |
|
Eskisehir Technical University Info Package