Neural Networks
Description: We will continue our course exploring neural networks. We will build on previous concepts to define and formalise these supervised model that constitute the foundations of the latest advances in Machine Learning. We will also introduce and formalise Deep Learning models, their architectures and implementation details.Department: Centro de Estudios y Asesorías en Estadística (CEASE)
Institution: Universidad de Nariño
Date: June 28, 2025
Hours: 4
From: 10:00 am
To: 12:00 am
Resources
Books
- Russell, S. and Norvig, P. (2010). Artificial Intelligence: A Modern Approach 3rd ed - Chapter 19 . Prentice Hall
- Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer - Chapter 5
- Deisenroth M. P. et. al. (2020). Mathematics for Machine Learning - Chapter 10
- Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep Learning. MIT Press - Chapters 6-9
- LeCun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444
- Nielsen, M. (2019). Neural Networks and Deep Learning
Papers and Reports
- Rumelhart, D. E., Hinton, G. E., Williams, R. J. (1986). Learning representations by back-propagating errors
- Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function
- Hornik, K., Stinchcombe, M., White, H. (1989). Multilayer feedforward networks are universal approximators
- LeCun, Y., et al. (1998). Gradient-based learning applied to document recognition
- Krizhevsky, A., Sutskever, I., Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks
- He, K., et al. (2016). Deep residual learning for image recognition
- Kingma, D. P., Ba, J. (2014). Adam: A method for stochastic optimization
- Srivastava, N., et al. (2014). Dropout: a simple way to prevent neural networks from overfitting
- Glorot, X., Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks
- He, K., et al. (2015). Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification