Preliminary Program

  • Introduction to Machine Learning
  • The Backpropagation Algorithm
  • Multi-Layer Perceptron Networks
  • Convolutional Neural Network
  • Resnet, Inception, ResNeXt
  • Auto Encoders
  • Region Based Convolutional Neural Networks (R-CNN)
  • Generative Adversarial Networks (GAN)



Bibliography:

Cobb, A. D., Roberts, S. J., Gal, Y., "Loss-Calibrated Approximate Inference in Bayesian Neural Networks", 2018 arXiv:1805.03901.
Goodfellow, I., et al., "Deep Learning", 2016. MIT press. http://www.deeplearningbook.org.
Hezaveh, Y. D., et al. "Fast automated analysis of strong gravitational lenses with convolutional neural networks." Nature 548.7669 (2017): 555.
Krizhevsky, A., Sutskever, I. and Hinton, G. E. ImageNet Classification with Deep Convolutional Neural Networks. NIPS 2012: Neural Information Processing Systems, Lake Tahoe, Nevada.
LeCun, Y., Bengio, Y. and Hinton, G., "Deep learning." Nature 521.7553 (2015): 436.
Schawinski, K., et al., "Generative adversarial networks recover features in astrophysical images of galaxies beyond the deconvolution limit", Monthly Notices of the Royal Astronomical Society: Letters, Volume 467, Issue 1, p.L110-L114.
Wu, J., "Introduction to convolutional neural networks." National Key Lab for Novel Software Technology, Nanjing University. China (2017).