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Link to original content: https://doi.org/10.1007/978-3-030-29516-5_78
Artificial Intelligence Teaching Methods in Higher Education | SpringerLink
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Artificial Intelligence Teaching Methods in Higher Education

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Intelligent Systems and Applications (IntelliSys 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1037))

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Abstract

Artificial Intelligence (AI) is emerging in all industries. In many technical positions, deep learning expertises have been a necessary condition for recruitment. Therefore, the deep learning theory and its practical applications are widely introduced in the AI-related course, named Media and Cognition course. Besides, the environmental awareness and other AI-related research topics, such as Human-Computer interaction and cognitive psychology, are also introduced in this course. Each student was asked to carry out at least one AI project to demonstrate what they have learned about deep learning. The good accuracy, speed and robustness were necessary for these projects. Further requirements were meaningful and efficient interaction with human or external environment. Many specific topics and solutions of AI applications, such as medical, transportation and finance, were proposed by students in the last three years, which implied that the contents and teaching method are practical and productive for novice learners.

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Notes

  1. 1.

    http://introtodeeplearning.com/.

  2. 2.

    http://cs231n.stanford.edu/.

References

  1. Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, Global edn. Education Limited, Malaysia (2016)

    MATH  Google Scholar 

  2. Nasrabadi, N.M.: Pattern recognition and machine learning. J. Electron. Imaging 16(4), 049901 (2007)

    Article  MathSciNet  Google Scholar 

  3. Goodfellow, I., Bengio, Y., Courville, A., et al.: Deep Learning. MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  4. Perisic, I. https://www.weforum.org/agenda/2018/09/artificial-intelligence-shaking-up-job-market/. Accessed 30 Dec 2018

  5. LeCun, Y., Bottou, L., Bengio, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  6. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, pp. 7132–7141 (2018)

    Google Scholar 

  7. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, Montréal, pp. 1097–1105 (2012)

    Google Scholar 

  8. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  9. Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, pp. 1–9 (2015)

    Google Scholar 

  10. He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, pp. 770–778 (2016)

    Google Scholar 

  11. Huang, G., Liu, Z., Van Der Maaten, L., et al.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, pp. 4700–4708. IEEE (2017)

    Google Scholar 

  12. Xie, S., Girshick, R., Dollár, P., et al.: Aggregated residual transformations for deep neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, pp. 5987–5995. IEEE (2017)

    Google Scholar 

  13. Lin, T.Y., Dollár, P., Girshick, R., et al.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, vol. 1, no. 2, p. 4. IEEE (2017)

    Google Scholar 

  14. Lin, T.Y., Goyal, P., Girshick, R., et al.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 1–10 (2018)

    Google Scholar 

  15. Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, Montréal, pp. 91–99 (2015)

    Google Scholar 

  16. Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, Boston, pp. 1440–1448 (2015)

    Google Scholar 

  17. Dai, J., Li, Y., He, K., et al.: R-FCN: object detection via region-based fully convolutional networks. In: Advances in Neural Information Processing Systems, Barcelona, pp. 379–387 (2016)

    Google Scholar 

  18. Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. arXiv preprint (2017)

    Google Scholar 

  19. Kong, T., Yao, A., Chen, Y., et al.: HyperNet: towards accurate region proposal generation and joint object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, pp. 845–853 (2016)

    Google Scholar 

  20. Chen, L.C., Papandreou, G., Kokkinos, I., et al.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018)

    Article  Google Scholar 

  21. He, K., Gkioxari, G., Dollár, P., et al.: Mask R-CNN. In: ICCV, Venice, pp. 2980–2988 (2017)

    Google Scholar 

  22. Hinton, G., Deng, L., Yu, D., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 29(6), 82–97 (2012)

    Article  Google Scholar 

  23. Richardson, F., Reynolds, D., Dehak, N.: Deep neural network approaches to speaker and language recognition. IEEE Signal Process. Lett. 22(10), 1671–1675 (2015)

    Article  Google Scholar 

  24. Shi, B., Yang, M., Wang, X., et al.: ASTER: an attentional scene text recognizer with flexible rectification. IEEE Trans. Pattern Anal. Mach. Intell. 1–14 (2018)

    Google Scholar 

  25. Rajpurkar, P., Irvin, J., Zhu, K., et al.: CheXNet: radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225 (2017)

  26. Robust Reading competition. http://rrc.cvc.uab.es/?ch=4&com=introduction/. Accessed 30 Dec 2018

  27. Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(11), 2298–2304 (2017)

    Article  Google Scholar 

  28. Jaderberg, M., Simonyan, K., Zisserman, A.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, Montréal, pp. 2017–2025 (2015)

    Google Scholar 

  29. Jaderberg, M., Simonyan, K., Vedaldi, A., et al.: Synthetic data and artificial neural networks for natural scene text recognition. arXiv preprint arXiv:1406.2227 (2014)

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Acknowledgments

Thanks to Tsinghua University Laboratory Innovation Funding.

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Correspondence to Yi Yang .

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Yang, Y., Sun, J., Huang, L. (2020). Artificial Intelligence Teaching Methods in Higher Education. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1037. Springer, Cham. https://doi.org/10.1007/978-3-030-29516-5_78

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